In today’s fast-paced digital world, harnessing the power of artificial intelligence (AI) can significantly enhance productivity and creativity across various domains. With the advent of advanced language models like ChatGPT, developers, marketers, data analysts, and professionals in numerous other fields can now leverage AI-generated prompts to spark innovative ideas and streamline their workflows.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
This document provides an introduction to machine learning concepts and tools. It begins with an overview of what will be covered in the course, including machine learning types, algorithms, applications, and mathematics. It then discusses data science concepts like feature engineering and the typical steps in a machine learning project, including collecting and examining data, fitting models, evaluating performance, and deploying models. Finally, it reviews common machine learning tools and terminologies and where to find datasets.
Prithvi Prabhu + Shivam Bansal, H2O.ai - Building Blocks for AI Applications ...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://www.youtube.com/watch?v=xAhQAYV5_PY&list=PLNtMya54qvOE3AvWRCNF2tybxNobUbAYp&index=3&t=2s
Bio: Prithvi is Chief of Technology, Applications at H2O.ai. Prithvi leads the design and development of “Q”, H2O.ai’s high scale exploratory data analysis and analytical application development platform.
Prithvi has been with H2O.ai since its early days and has been responsible for several products including Driverless AI (our flagship automatic machine learning platform), Steam (distributed cluster management, model management and deployment for H2O), H2O.js (Javascript transpiler for H2O’s distributed runtime), Play (on-demand cloud provisioning system for H2O), Flow (a hybrid GUI/REPL/Notebook for H2O) and Lightning (statistical graphics for H2O).
Bio: Shivam Bansal is a Data Scientist at H2O.ai and Kaggle Grandmaster in Kernels Section. He is the three times winner of Kaggle’s Data Science for Good Competition and winner of multiple other offline AI and Data Science competitions.
Shivam has extensive cross-industry and hands-on experience in building data science products. He has helped clients in the Insurance, Healthcare, Banking, and Retail domains to solve unstructured data science problems by building end to end pipelines and solutions.
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Simplified Machine Learning, Text, and Graph Analytics with Pivotal GreenplumVMware Tanzu
Data is at the center of digital transformation; using data to drive action is how transformation happens. But data is messy, and it’s everywhere. It’s in the cloud and on-premises. It’s in different types and formats. By the time all this data is moved, consolidated, and cleansed, it can take weeks to build a predictive model.
Even with data lakes, efficiently integrating multi-structured data from different data sources and streams is a major challenge. Enterprises struggle with a stew of data integration tools, application integration middleware, and various data quality and master data management software. How can we simplify this complexity to accelerate and de-risk analytic projects?
The data warehouse—once seen as only for traditional business intelligence applications — has learned new tricks. Join James Curtis from 451 Research and Pivotal’s Bob Glithero for an interactive discussion about the modern analytic data warehouse. In this webinar, we’ll share insights such as:
- Why after much experimentation with other architectures such as data lakes, the data warehouse has reemerged as the platform for integrated operational analytics
- How consolidating structured and unstructured data in one environment—including text, graph, and geospatial data—makes in-database, highly parallel, analytics practical
- How bringing open-source machine learning, graph, and statistical methods to data accelerates analytical projects
- How open-source contributions from a vibrant community of Postgres developers reduces adoption risk and accelerates innovation
We thank you in advance for joining us.
Presenter : Bob Glithero, PMM, Pivotal and James Curtis Senior Analyst, 451 Research
ChatGPT for Data Science Projects presentation introduces the capabilities of ChatGPT, an AI large language generative model that can assist data scientists in various stages of a project. The presentation covers three main topics: data exploration and analysis, building predictive models, and model evaluation and selection. Each topic includes examples of questions that can be asked of ChatGPT to generate insights and assist with decision-making. The presentation also includes a section on setting up ChatGPT for data analysis, covering topics such as installing required libraries, preparing data, and initializing ChatGPT. This presentation is ideal for anyone interested in exploring the capabilities of AI language models in data science projects.
Data - Science and Engineering slide at Bandungpy Sharing SessionHendri Karisma
This document discusses data science and engineering roles. It defines data scientist and data engineer roles. Data scientists analyze large amounts of data to answer questions and drive organizational strategy, while data engineers build systems to collect, manage and transform raw data for analysis. The document also discusses the role of AI engineers, who develop complex algorithms and infrastructure for AI systems. It provides examples of responsibilities for each role and the data science experiment process.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
This document provides an introduction to machine learning concepts and tools. It begins with an overview of what will be covered in the course, including machine learning types, algorithms, applications, and mathematics. It then discusses data science concepts like feature engineering and the typical steps in a machine learning project, including collecting and examining data, fitting models, evaluating performance, and deploying models. Finally, it reviews common machine learning tools and terminologies and where to find datasets.
Prithvi Prabhu + Shivam Bansal, H2O.ai - Building Blocks for AI Applications ...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://www.youtube.com/watch?v=xAhQAYV5_PY&list=PLNtMya54qvOE3AvWRCNF2tybxNobUbAYp&index=3&t=2s
Bio: Prithvi is Chief of Technology, Applications at H2O.ai. Prithvi leads the design and development of “Q”, H2O.ai’s high scale exploratory data analysis and analytical application development platform.
Prithvi has been with H2O.ai since its early days and has been responsible for several products including Driverless AI (our flagship automatic machine learning platform), Steam (distributed cluster management, model management and deployment for H2O), H2O.js (Javascript transpiler for H2O’s distributed runtime), Play (on-demand cloud provisioning system for H2O), Flow (a hybrid GUI/REPL/Notebook for H2O) and Lightning (statistical graphics for H2O).
Bio: Shivam Bansal is a Data Scientist at H2O.ai and Kaggle Grandmaster in Kernels Section. He is the three times winner of Kaggle’s Data Science for Good Competition and winner of multiple other offline AI and Data Science competitions.
Shivam has extensive cross-industry and hands-on experience in building data science products. He has helped clients in the Insurance, Healthcare, Banking, and Retail domains to solve unstructured data science problems by building end to end pipelines and solutions.
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Simplified Machine Learning, Text, and Graph Analytics with Pivotal GreenplumVMware Tanzu
Data is at the center of digital transformation; using data to drive action is how transformation happens. But data is messy, and it’s everywhere. It’s in the cloud and on-premises. It’s in different types and formats. By the time all this data is moved, consolidated, and cleansed, it can take weeks to build a predictive model.
Even with data lakes, efficiently integrating multi-structured data from different data sources and streams is a major challenge. Enterprises struggle with a stew of data integration tools, application integration middleware, and various data quality and master data management software. How can we simplify this complexity to accelerate and de-risk analytic projects?
The data warehouse—once seen as only for traditional business intelligence applications — has learned new tricks. Join James Curtis from 451 Research and Pivotal’s Bob Glithero for an interactive discussion about the modern analytic data warehouse. In this webinar, we’ll share insights such as:
- Why after much experimentation with other architectures such as data lakes, the data warehouse has reemerged as the platform for integrated operational analytics
- How consolidating structured and unstructured data in one environment—including text, graph, and geospatial data—makes in-database, highly parallel, analytics practical
- How bringing open-source machine learning, graph, and statistical methods to data accelerates analytical projects
- How open-source contributions from a vibrant community of Postgres developers reduces adoption risk and accelerates innovation
We thank you in advance for joining us.
Presenter : Bob Glithero, PMM, Pivotal and James Curtis Senior Analyst, 451 Research
ChatGPT for Data Science Projects presentation introduces the capabilities of ChatGPT, an AI large language generative model that can assist data scientists in various stages of a project. The presentation covers three main topics: data exploration and analysis, building predictive models, and model evaluation and selection. Each topic includes examples of questions that can be asked of ChatGPT to generate insights and assist with decision-making. The presentation also includes a section on setting up ChatGPT for data analysis, covering topics such as installing required libraries, preparing data, and initializing ChatGPT. This presentation is ideal for anyone interested in exploring the capabilities of AI language models in data science projects.
Data - Science and Engineering slide at Bandungpy Sharing SessionHendri Karisma
This document discusses data science and engineering roles. It defines data scientist and data engineer roles. Data scientists analyze large amounts of data to answer questions and drive organizational strategy, while data engineers build systems to collect, manage and transform raw data for analysis. The document also discusses the role of AI engineers, who develop complex algorithms and infrastructure for AI systems. It provides examples of responsibilities for each role and the data science experiment process.
Building an enterprise Natural Language Search Engine with ElasticSearch and ...Debmalya Biswas
Presented at Berlin Buzzwords 2019
https://berlinbuzzwords.de/19/session/building-enterprise-natural-language-search-engine-elasticsearch-and-facebooks-drqa
Multiplatform Spark solution for Graph datasources by Javier DominguezBig Data Spain
This document summarizes a presentation given by Javier Dominguez at Big Data Spain about Stratio's multiplatform solution for graph data sources. It discusses graph use cases, different data stores like Spark, GraphX, GraphFrames and Neo4j. It demonstrates the machine learning life cycle using a massive dataset from Freebase, running queries and algorithms. It shows notebooks and a business example of clustering bank data using Jaccard distance and connected components. The presentation concludes with future directions like a semantic search engine and applying more machine learning algorithms.
Talk on Data Discovery and Metadata by Mark Grover from July 2019.
Goes into detail of the problem, build/buy/adopt analysis and Lyft's solution - Amundsen, along with thoughts on the future.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
This document discusses DevOps and MLOps practices for machine learning models. It outlines that while ML development shares some similarities with traditional software development, such as using version control and CI/CD pipelines, there are also key differences related to data, tools, and people. Specifically, ML requires additional focus on exploratory data analysis, feature engineering, and specialized infrastructure for training and deploying models. The document provides an overview of how one company structures their ML team and processes.
This document discusses demystifying data science. It begins by introducing the speaker and their background in data science. It then discusses common misconceptions about data science, noting that it is more than just statistics, machine learning, big data, or business analytics. The document outlines the full data science process from exploratory analysis to modeling to testing and evaluation. It emphasizes the importance of a scientific approach and focusing on solving business problems. Finally, it discusses best practices for developing data products and the ideal skillset of a data science team.
Kognitio Webinar: Showcasing the Data Scientist Lab functionality with External Scripting and how it can be used to run ‘R’ in an MPP environment
April 18, 8:00am pst, 11:00am est, 4pm bst, 5pm cest
Duration: 45mins plus Q&A
Register
Dr. Sharon Kirkham, Principal, Kognitio Analytics Center of Excellence, showcases the power of external scripting with a demonstration of the ‘R’ statistical language, running in the massively parallel Kognitio Analytical Platform environment.
The document provides an overview of an introductory course on artificial intelligence (AI), machine learning (ML), and deep learning (DL). Some key details include:
- The course title is AI (Machine Learning / Deep Learning) and runs for 6 months.
- The course aims to provide employable skills in AI programming, data science, deep learning, computer vision, natural language processing, and ML operations.
- Learning outcomes cover topics like AI fundamentals, data analytics, deep learning, computer vision, natural language processing, and core skills.
- The course prepares students for jobs like Python developer, data analyst, machine learning engineer, and more.
MOPs & ML Pipelines on GCP - Session 6, RGDCgdgsurrey
MLOps Lifecycle
ML problem framing
ML solution architecture
Data preparation and processing
ML model development
ML pipeline automation and orchestration
ML solution monitoring, optimization, and maintenance
Data Science Challenge presentation given to the CinBITools Meetup GroupDoug Needham
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires social network analysis to recommend users to follow on a social media platform based on click data. The document discusses the approaches, tools, and algorithms used to solve each problem at scale using Apache Spark and Hadoop technologies.
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires analyzing a social network graph to recommend users to follow. The document discusses the approaches, tools, and results for each problem.
This document provides an overview of machine learning in Python using key Python libraries. It discusses popular Python libraries for machine learning like NumPy, SciPy, Pandas, Matplotlib and scikit-learn. It outlines the typical steps in a machine learning project including defining the problem, preparing and summarizing data, evaluating algorithms, and presenting results. It also introduces the Iris dataset as a sample classification dataset and discusses loading, handling and visualizing sample data for a machine learning project in Python.
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
In the dynamic field of DevOps, the quest for efficiency and productivity is endless. This talk introduces a revolutionary toolkit: Large Language Models (LLMs), including ChatGPT, Gemini, and Claude, extending far beyond traditional coding assistance. We'll explore how LLMs can automate not just code generation, but also transform day-to-day operations such as crafting compelling cover letters for TPS reports, streamlining client communications, and architecting innovative DevOps solutions. Attendees will learn effective prompting strategies and examine real-life use cases, demonstrating LLMs' potential to redefine productivity in the DevOps landscape. Join us to discover how to harness the power of LLMs for a comprehensive productivity boost across your DevOps activities.
Avoid hiring data ninja rockstars: how to build effective data teamsJodieBurchell1
Big data, data science, and machine learning rose to prominence around a decade ago, and have become cemented in the tech landscape as the size and complexity of data continues to increase. However, many companies still are confused about how to best make use of their data, and try to hire "all-in-one" superstars who can do everything from research to creating efficient ETLs to maintaining machine learning projects in production. In this talk, we'll explain how most data science or machine learning work should be a collaboration between dedicated data scientists and data engineers. We'll talk about the core responsibilities of each role, where they can each add the most value to companies, and also where their roles overlap. We'll also discuss where specializations such as machine learning scientists, machine learning engineers, DBAs and ML ops fit in, and whether the future seems to be heading more towards generalization or specialization. We'll end with recommendations on how you can build the best combination of these roles for your company's needs.
Start your Data Science career journey with an extensive & practical Data Science course designed for young professionals and recent college graduates. We provide in-depth knowledge of Python’s data analytics tools and techniques in this Data Science certification program.
The document discusses how graphs can enhance AI and machine learning by providing structured connectivity data and features derived from graph algorithms, embeddings, and neural networks. It outlines steps for doing graph data science, including building knowledge graphs, developing graph-based features, and using graph neural networks. The document also provides examples of applying these graph techniques across domains like financial services, healthcare, and recommendations.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
Webinar - Unleash AI power with MySQL and MindsDBFederico Razzoli
MindsDB enormously simplifies the process of making machine learning based predictions. Intead of developing a model and prepare data, you can connect MindsDB to an external data source (such as MySQL, PostgreSQL, other databases, or APIs) and run SQL queries about the future. Any AI engine (predictive algorithm) can be used.
80 Best Prompts for AI Art like Midjourney, Bing, DALL-E, and Limewire.pdfiSEO AI
The advent of AI art has transformed the creative landscape, offering artists and enthusiasts a powerful set of tools to generate stunning visuals with ease. Platforms like Midjourney, Bing, DALL-E, and Limewire have become popular choices for creating AI art, each with its unique capabilities and style. To get the most out of these tools, understanding how to craft effective prompts is essential. Here, we explore some of the best prompts to use with these AI art platforms.
5 Ways Interactive Content Helps Publishers Pump Up Web Revenues - iSEO AI.pdfiSEO AI
In the competitive world of online publishing, finding effective strategies to boost web revenues is crucial for sustained success. Interactive content has emerged as a powerful tool that not only engages audiences but also enhances monetization opportunities for publishers. Here are five ways interactive content can help publishers increase their web revenues:
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Building an enterprise Natural Language Search Engine with ElasticSearch and ...Debmalya Biswas
Presented at Berlin Buzzwords 2019
https://berlinbuzzwords.de/19/session/building-enterprise-natural-language-search-engine-elasticsearch-and-facebooks-drqa
Multiplatform Spark solution for Graph datasources by Javier DominguezBig Data Spain
This document summarizes a presentation given by Javier Dominguez at Big Data Spain about Stratio's multiplatform solution for graph data sources. It discusses graph use cases, different data stores like Spark, GraphX, GraphFrames and Neo4j. It demonstrates the machine learning life cycle using a massive dataset from Freebase, running queries and algorithms. It shows notebooks and a business example of clustering bank data using Jaccard distance and connected components. The presentation concludes with future directions like a semantic search engine and applying more machine learning algorithms.
Talk on Data Discovery and Metadata by Mark Grover from July 2019.
Goes into detail of the problem, build/buy/adopt analysis and Lyft's solution - Amundsen, along with thoughts on the future.
1) The document discusses a self-study approach to learning data science through project-based learning using various online resources.
2) It recommends breaking down projects into 5 steps: defining problems/solutions, data extraction/preprocessing, exploration/engineering, model implementation, and evaluation.
3) Each step requires different skillsets from domains like statistics, programming, SQL, visualization, mathematics, and business knowledge.
This document discusses DevOps and MLOps practices for machine learning models. It outlines that while ML development shares some similarities with traditional software development, such as using version control and CI/CD pipelines, there are also key differences related to data, tools, and people. Specifically, ML requires additional focus on exploratory data analysis, feature engineering, and specialized infrastructure for training and deploying models. The document provides an overview of how one company structures their ML team and processes.
This document discusses demystifying data science. It begins by introducing the speaker and their background in data science. It then discusses common misconceptions about data science, noting that it is more than just statistics, machine learning, big data, or business analytics. The document outlines the full data science process from exploratory analysis to modeling to testing and evaluation. It emphasizes the importance of a scientific approach and focusing on solving business problems. Finally, it discusses best practices for developing data products and the ideal skillset of a data science team.
Kognitio Webinar: Showcasing the Data Scientist Lab functionality with External Scripting and how it can be used to run ‘R’ in an MPP environment
April 18, 8:00am pst, 11:00am est, 4pm bst, 5pm cest
Duration: 45mins plus Q&A
Register
Dr. Sharon Kirkham, Principal, Kognitio Analytics Center of Excellence, showcases the power of external scripting with a demonstration of the ‘R’ statistical language, running in the massively parallel Kognitio Analytical Platform environment.
The document provides an overview of an introductory course on artificial intelligence (AI), machine learning (ML), and deep learning (DL). Some key details include:
- The course title is AI (Machine Learning / Deep Learning) and runs for 6 months.
- The course aims to provide employable skills in AI programming, data science, deep learning, computer vision, natural language processing, and ML operations.
- Learning outcomes cover topics like AI fundamentals, data analytics, deep learning, computer vision, natural language processing, and core skills.
- The course prepares students for jobs like Python developer, data analyst, machine learning engineer, and more.
MOPs & ML Pipelines on GCP - Session 6, RGDCgdgsurrey
MLOps Lifecycle
ML problem framing
ML solution architecture
Data preparation and processing
ML model development
ML pipeline automation and orchestration
ML solution monitoring, optimization, and maintenance
Data Science Challenge presentation given to the CinBITools Meetup GroupDoug Needham
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires social network analysis to recommend users to follow on a social media platform based on click data. The document discusses the approaches, tools, and algorithms used to solve each problem at scale using Apache Spark and Hadoop technologies.
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires analyzing a social network graph to recommend users to follow. The document discusses the approaches, tools, and results for each problem.
This document provides an overview of machine learning in Python using key Python libraries. It discusses popular Python libraries for machine learning like NumPy, SciPy, Pandas, Matplotlib and scikit-learn. It outlines the typical steps in a machine learning project including defining the problem, preparing and summarizing data, evaluating algorithms, and presenting results. It also introduces the Iris dataset as a sample classification dataset and discusses loading, handling and visualizing sample data for a machine learning project in Python.
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
In the dynamic field of DevOps, the quest for efficiency and productivity is endless. This talk introduces a revolutionary toolkit: Large Language Models (LLMs), including ChatGPT, Gemini, and Claude, extending far beyond traditional coding assistance. We'll explore how LLMs can automate not just code generation, but also transform day-to-day operations such as crafting compelling cover letters for TPS reports, streamlining client communications, and architecting innovative DevOps solutions. Attendees will learn effective prompting strategies and examine real-life use cases, demonstrating LLMs' potential to redefine productivity in the DevOps landscape. Join us to discover how to harness the power of LLMs for a comprehensive productivity boost across your DevOps activities.
Avoid hiring data ninja rockstars: how to build effective data teamsJodieBurchell1
Big data, data science, and machine learning rose to prominence around a decade ago, and have become cemented in the tech landscape as the size and complexity of data continues to increase. However, many companies still are confused about how to best make use of their data, and try to hire "all-in-one" superstars who can do everything from research to creating efficient ETLs to maintaining machine learning projects in production. In this talk, we'll explain how most data science or machine learning work should be a collaboration between dedicated data scientists and data engineers. We'll talk about the core responsibilities of each role, where they can each add the most value to companies, and also where their roles overlap. We'll also discuss where specializations such as machine learning scientists, machine learning engineers, DBAs and ML ops fit in, and whether the future seems to be heading more towards generalization or specialization. We'll end with recommendations on how you can build the best combination of these roles for your company's needs.
Start your Data Science career journey with an extensive & practical Data Science course designed for young professionals and recent college graduates. We provide in-depth knowledge of Python’s data analytics tools and techniques in this Data Science certification program.
The document discusses how graphs can enhance AI and machine learning by providing structured connectivity data and features derived from graph algorithms, embeddings, and neural networks. It outlines steps for doing graph data science, including building knowledge graphs, developing graph-based features, and using graph neural networks. The document also provides examples of applying these graph techniques across domains like financial services, healthcare, and recommendations.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch: https://bit.ly/2DYsUhD
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- How Prologis accelerated their use of Machine Learning with data virtualization
Webinar - Unleash AI power with MySQL and MindsDBFederico Razzoli
MindsDB enormously simplifies the process of making machine learning based predictions. Intead of developing a model and prepare data, you can connect MindsDB to an external data source (such as MySQL, PostgreSQL, other databases, or APIs) and run SQL queries about the future. Any AI engine (predictive algorithm) can be used.
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The advent of AI art has transformed the creative landscape, offering artists and enthusiasts a powerful set of tools to generate stunning visuals with ease. Platforms like Midjourney, Bing, DALL-E, and Limewire have become popular choices for creating AI art, each with its unique capabilities and style. To get the most out of these tools, understanding how to craft effective prompts is essential. Here, we explore some of the best prompts to use with these AI art platforms.
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UiPath Test Automation using UiPath Test Suite series, part 6
1111 ChatGPT Prompts PDF Free Download - Prompts for ChatGPT
1. Home Prompts For AI Prompts For ChatGPT
110 Smart ChatGPTPrompts ForData
Science
In the realm of data science, where insights drive innovation and decision-
making, the ability to generate meaningful analyses and solutions
efficiently is paramount. Smart ChatGPT prompts have emerged as
valuable tools for data scientists, offering a versatile approach to tackling
various tasks and challenges in the field. From exploratory data analysis
and model development to report generation and presentation, ChatGPT
prompts can streamline workflows, spark creativity, and accelerate
problem-solving. Let’s explore how data scientists can effectively leverage
Smart ChatGPT prompts to elevate their work and achieve better
outcomes.
Understanding Smart ChatGPT Prompts for Data Science
Smart ChatGPT prompts are prompts or queries that guide the AI model
to generate responses, insights, or solutions related to data science tasks
and projects. These prompts can range from simple inquiries about data
analysis techniques to complex requests for model development or
PROMPTS FOR CHATGPT
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Index
2. visualization recommendations. By providing specific prompts, data
scientists can harness the power of ChatGPT to augment their analytical
capabilities and address various data-related challenges effectively.
Enhancing Exploratory Data Analysis (EDA) and Data Cleaning
Exploratory Data Analysis (EDA) is a crucial phase in the data science
process, allowing practitioners to understand the structure, patterns, and
relationships within datasets. Smart ChatGPT prompts can aid data
scientists in conducting EDA by generating descriptive statistics,
visualizations, and insights that illuminate key aspects of the data. For
example, prompts like “Explore trends in customer purchasing behavior”
or “Identify outliers in financial transaction data” can guide the AI model
to provide relevant analyses and visualizations, facilitating deeper
insights and informed decision-making.
Accelerating Model Development and Optimization
Model development and optimization are central to data science projects,
where the goal is to build predictive models that deliver accurate and
actionable insights. Smart ChatGPT prompts can expedite this process by
generating code snippets, algorithm recommendations, and parameter
tuning strategies tailored to specific modeling tasks. Data scientists can
input prompts such as “Recommend algorithms for time series
forecasting” or “Optimize hyperparameters for neural network
classification,” leveraging ChatGPT’s expertise to streamline model
development and enhance predictive performance.
Automating Report Generation and Documentation
Effective communication of findings and insights is essential in data
science projects, enabling stakeholders to understand results and make
informed decisions. Smart ChatGPT prompts can automate report
generation and documentation by generating summaries, interpretations,
and visual representations of analysis results. By inputting prompts like
“Create a summary report of regression analysis findings” or “Generate
visualizations for presentation slides on market trends,” data scientists
can expedite the reporting process and communicate complex
information effectively to diverse audiences.
Providing Insights and Recommendations
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3. In addition to supporting specific tasks, Smart ChatGPT prompts can
provide valuable insights and recommendations on data science
methodologies, best practices, and emerging trends. Data scientists can
input prompts such as “Suggest techniques for feature engineering in
machine learning” or “Provide resources for learning about deep learning
architectures,” tapping into ChatGPT’s knowledge base to expand their
expertise and stay updated on the latest advancements in the field.
Tips for Effective Use of Smart ChatGPT Prompts in Data Science
1. Be Specific: Clearly define your data science task or problem when
formulating prompts to ensure relevant and actionable responses.
2. Iterate and Refine: Experiment with different prompts and refine
them based on the quality and relevance of the generated
responses.
3. Validate Outputs: Verify the accuracy and validity of AI-generated
outputs through manual review, testing, or validation against ground
truth data.
4. Combine with Domain Knowledge: Supplement AI-generated
insights with domain expertise and critical thinking to ensure robust
analysis and interpretation.
Here are the best ChatGPT Prompts for Data Science:
Table of Contents
■ ChatGPT for Data Science Building Machine Learning Models
■ 1. Train a Classification Model
■ 2. Automatic Machine Learning with TPOT
■ ChatGPT for Data Science Data Exploration and Visualization
■ 3. Explore a Dataset
■ 4. Visualize Data with Matplotlib
■ Code Optimization and Improvement
■ 5. Improve Code Speed
■ 6. Optimize Pandas Code
■ ChatGPT for Data Science Writing and Translating Code
■ 7. Write a Regex in Python
■ 8. Translate Python to R
■ Understanding and Explaining Code
■ 9. Explain Python Code
■ 10. Explain SQL Code
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Index
4. ■ ChatGPT for Data Science Code Debugging and Troubleshooting
■ 11. Debug Python Code
■ 12. Correct SQL Code
■ ChatGPT for Data Science Machine Learning Model Interpretation
■ 13. Get Feature Importance
■ 14. Explain Model with SHAP
■ ChatGPT for Data Science Working with Time Series Data
■ 15. Time Series Decomposition
■ 16. Time Series Forecasting with ARIMA
■ ChatGPT for Data Science Deep Learning and Neural Networks
■ 17. Build a Simple Neural Network
■ 18. Transfer Learning with Pretrained Models
■ ChatGPT for Data Science Natural Language Processing
■ 19. Text Classification with BERT
■ 20. Named Entity Recognition with SpaCy
■ ChatGPT for Data Science Recommender Systems
■ 21. Collaborative Filtering with Surprise
■ 22. Content-Based Recommender
■ ChatGPT for Data Science Data Wrangling
■ 23. Clean and Preprocess Text Data
■ 24. Combine Multiple Datasets
■ ChatGPT for Data Science Data Ethics and Bias
■ 25. Identify and Mitigate Bias in AI
■ 26. Privacy-Preserving Techniques in Data Science
■ ChatGPT for Data Science Big Data and Distributed Computing
■ 27. Analyze Big Data with Dask
■ 28. Distributed Machine Learning with Apache Spark
■ ChatGPT for Data Science Data Science Career and Education
■ 29. Advice for Aspiring Data Scientists
■ 30. Best Data Science Courses and Resources
■ ChatGPT for Data Science Other Data Science Tools
■ 31. Geospatial Analysis with Python
■ 32. Anomaly Detection in Time Series Data
■ 33. Text Summarization with Machine Learning
■ 34. A/B Testing and Experimental Design
■ 35. Creating Interactive Visualizations with Plotly
■ WRITE PYTHON
■ 1. Train Classification Model
■ 2. Automatic Machine Learning
■ 3. Tune Hyperparameter
■ 4. Explore Data
■ 5. Generate Data
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Index
5. ■ 6. Write Regex
■ 7. Train Time Series
■ 8. Address Imbalance Data
■ 9. Get Feature Importance
■ 10. Visualize Data with Matplotlib
■ 11. Visualize Image Grid Matplotlib
■ 12. Explain Model with Lime
■ 13. Explain Model with Shap
■ 14. Write Multithreaded Functions
■ 15. Compare Function Speed
■ 16. Create NumPy Array
■ 17. Write Unit Test
■ 18. Validate Column
■ EXPLAIN CODE
■ 19. Explain Python
■ 20. Explain SQL
■ 21. Explain Google Sheets Formula
■ OPTIMIZE CODE
■ 22. Improve Code Speed
■ 23. Optimize Pandas
■ 24. Optimize Pandas Again
■ 25. Optimize Python
■ 26. Optimize SQL
■ 27. Simplify Python
■ FORMAT CODE
■ 28. Write Documentation
■ 29. Improve Readability
■ 30. Format SQL
■ TRANSLATE CODE
■ 31. Translate Between DBMS
■ 32. Translate Python to R
■ 33. Translate R to Python
■ EXPLAIN CONCEPTS
■ 34. Explain to Five-Year-Old
■ 35. Explain to Undergraduate
■ 36. Explain to Professor
■ 37. Explain to Business Stakeholder
■ 38. Explain Like Stackoverflow
■ SUGGEST IDEAS
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Index
6. ChatGPTforData Science: Building
Machine Learning Models
1.Train a Classification Model
Prompt: I want you to act as a data scientist and code for me. I have a
dataset of [describe dataset]. Please build a machine learning model that
predicts [target variable].
2.Automatic Machine LearningwithTPOT
■ 39. Suggest Edge Cases
■ 40. Suggest Dataset
■ 41. Suggest Portfolio Ideas
■ 42. Suggest Resources
■ 43. Suggest Time Complexity
■ 44. Suggest Feature Engineering
■ 45. Suggest Ab Testing Steps
■ 46. Career Coaching
■ TROUBLESHOOT PROBLEM
■ 47. Correct Own ChatGPT Code
■ 48. Correct Python Code
■ 49. Correct SQL Code
■ 50. Troubleshoot PowerBI Model
■ WRITE SQL
■ 51. Create Running Average
■ 52. Solve Leetcode Question
■ WRITE OTHER CODE
■ 53. Write Google Sheets Formula
■ 54. Write R
■ 55. Write Shell
■ 56. Write VBA
■ MISC
■ 57. Format Tables
■ 58. Summarize Book
■ 59. Summarize Paper
■ 60. Provide Emotional Support
■ Other useful ChatGPT prompts for Data Science
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Index
7. Prompt: I want you to act as an automatic machine learning (AutoML) bot
using TPOT for me. I am working on a model that predicts […]. Please
write Python code to find the best classification model with the highest
AUC score on the test set.
ChatGPTforData Science: Data
Exploration andVisualization
3. Explore a Dataset
Prompt: I want you to act as a data scientist and code for me. I have a
dataset of [describe dataset]. Please write code for data visualization and
exploration.
4.Visualize Datawith Matplotlib
Prompt: I want you to act as a coder in Python. I have a dataset [name]
with columns [name]. [Describe graph requirements]
Code Optimization and Improvement
5. Improve Code Speed
Prompt: I want you to act as a software developer. Please help me
improve the time complexity of the code below. [Insert code]
6. Optimize Pandas Code
Prompt: I want you to act as a code optimizer. Can you point out what’s
wrong with the following pandas code and optimize it? [Insert code here]
ChatGPTforData Science:Writing and
Translating Code
7.Write a Regex in Python
Prompt: I want you to act as a coder. Please write me a regex in Python
that [describe regex]
8.Translate Pythonto R
Prompt: I want you to act as a code translator. Can you please convert the
following code from Python to R? [Insert code]
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Index
8. Understanding and Explaining Code
9. Explain Python Code
Prompt: I want you to act as a code explainer. What is this code doing?
[Insert code]
10. Explain SQLCode
Prompt: I want you to act as a data science instructor. Can you please
explain to me what this SQL code is doing? [Insert SQL code]
ChatGPTforData Science: Code
Debugging andTroubleshooting
11. Debug Python Code
Prompt: I want you to act as a software developer. This code is supposed
to [expected function]. Please help me debug this Python code that
cannot be run. [Insert function]
12. Correct SQLCode
Prompt: I want you to act as a SQL code corrector. This code does not run
in [your DBMS, e.g. PostgreSQL]. Can you correct it for me? [SQL code
here]
ChatGPTforData Science: Machine
Learning Model Interpretation
13. Get Feature Importance
Prompt: I want you to act as a data scientist and explain the model’s
results. I have trained a decision tree model and I would like to find the
most important features. Please write the code.
14. Explain Modelwith SHAP
Prompt: I want you to act as a data scientist and explain the model’s
results. I have trained a scikit-learn XGBoost model and I would like to
explain the output using a series of plots with SHAP. Please write the
code.
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Index
9. ChatGPTforData Science:Working
withTime Series Data
15.Time Series Decomposition
Prompt: I want you to act as a data scientist and code for me. I have a
time series dataset of [describe dataset]. Please perform a time series
decomposition and plot the components.
16.Time Series ForecastingwithARIMA
Prompt: I want you to act as a data scientist and code for me. I have a
time series dataset of [describe dataset]. Please help me build an ARIMA
model to forecast the data.
ChatGPTforData Science: Deep
Learning and Neural Networks
17. Build a Simple Neural Network
Prompt: I want you to act as a deep learning expert. Please write code to
create a simple neural network with TensorFlow for [describe task].
18.TransferLearningwith Pretrained Models
Prompt: I want you to act as a deep learning expert. I have a dataset
[describe dataset]. Please write code to perform transfer learning using a
pretrained model from TensorFlow Hub.
ChatGPTforData Science: Natural
Language Processing
19.Text Classificationwith BERT
Prompt: I want you to act as a natural language processing expert. I have
a text dataset [describe dataset]. Please help me build a text
classification model using BERT.
20. Named EntityRecognitionwith SpaCy
Prompt: I want you to act as a natural language processing expert. I have
a text dataset [describe dataset]. Please help me extract named entities
using SpaCy.
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Index
10. ChatGPTforData Science:
RecommenderSystems
21. Collaborative Filteringwith Surprise
Prompt: I want you to act as a recommender systems expert. I have a
dataset of user-item ratings. Please help me build a collaborative filtering
model using the Surprise library.
22. Content-Based Recommender
Prompt: I want you to act as a recommender systems expert. I have a
dataset of items with metadata [describe dataset]. Please help me build a
content-based recommender.
ChatGPTforData Science: Data
Wrangling
23. Clean and PreprocessText Data
Prompt: I want you to act as a data scientist and code for me. I have a
dataset of text data [describe dataset]. Please help me clean and
preprocess the data for further analysis.
24. Combine Multiple Datasets
Prompt: I want you to act as a data scientist and code for me. I have
several datasets with different structures [describe datasets]. Please help
me combine them into a single dataset for analysis.
ChatGPTforData Science: Data Ethics
and Bias
25. Identifyand Mitigate Bias inAI
Prompt: I want you to act as a data ethics expert. How can we identify
and mitigate biases in AI algorithms?
26. Privacy-PreservingTechniques in Data Science
Prompt: I want you to act as a data privacy expert. What are some
privacy-preserving techniques we can use in data science projects?
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Index
11. ChatGPTforData Science: Big Data and
Distributed Computing
27.Analyze Big Datawith Dask
Prompt: I want you to act as a big data expert. I have a large dataset
[describe dataset]. Please help me analyze it using Dask.
28. Distributed Machine LearningwithApache Spark
Prompt: I want you to act as a big data expert. I have a dataset [describe
dataset]. Please help me build a machine learning model using Apache
Spark.
ChatGPTforData Science: Data
Science Careerand Education
29.AdviceforAspiring Data Scientists
Prompt: I want you to act as a data science career coach. What advice
would you give to aspiring data scientists?
30. Best Data Science Courses and Resources
Prompt: I want you to act as a data science education expert. What are
the best courses and resources for learning data science?
ChatGPTforData Science: OtherData
ScienceTools
31. GeospatialAnalysiswith Python
Prompt: I want you to act as a geospatial expert. I have a dataset with
geospatial information [describe dataset]. Please help me perform
geospatial analysis using Python libraries.
32.AnomalyDetection inTime Series Data
Prompt: I want you to act as a data scientist and code for me. I have a
time series dataset of [describe dataset]. Please help me identify
anomalies in the data.
33.Text Summarizationwith Machine Learning
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Index
12. Prompt: I want you to act as a natural language processing expert. I have
a large text dataset [describe dataset]. Please help me build a model for
text summarization.
34.A/BTesting and Experimental Design
Prompt: I want you to act as a data scientist and code for me. I have a
dataset of user behavior [describe dataset]. Please help me design and
analyze an A/B test to optimize a specific metric.
35. Creating InteractiveVisualizationswith Plotly
Prompt: I want you to act as a data visualization expert. I have a dataset
[describe dataset]. Please help me create interactive visualizations using
Plotly.
Source: https://docs.kanaries.net/articles/chatgpt-prompt-data-scientist
WRITE PYTHON
1.Train Classification Model
Prompt: I want you to act as a data scientist and code for
me. I have a dataset of [describe dataset] . Please
build a machine learning model that predicts [target
variable] .
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Index
13. 2.Automatic Machine Learning
3.Tune Hyperparameter
4. Explore Data
Prompt: I want you to act as an automatic machine
learning (AutoML) bot using TPOT for me. I am working on
a model that predicts [...] . Please write Python code to
find the best classification model with the highest AUC
score on the test set.
Prompt: I want you to act as a data scientist and code for
me. I have trained a [model name] . Please write the code
to tune the hyperparameters.
Prompt: I want you to act as a data scientist and code for
me. I have a dataset of [describe dataset] . Please
write code for data visualisation and exploration.
→
Index
14. 5. Generate Data
6.Write Regex
7.TrainTime Series
Prompt: I want you to act as a fake data generator. I need a
dataset that has x rows and y columns: [insert column
names]
Prompt: I want you to act as a coder. Please write me a
regex in Python that [describe regex]
Prompt: I want you to act as a data scientist and code for
me. I have a time series dataset [describe dataset] .
Please build a machine learning model that
predicts [target variable] . Please use [time
range] as train and [time range] as validation.
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Index
15. 8.Address Imbalance Data
9. Get Feature Importance
10.Visualize Datawith Matplotlib
Prompt: I want you to act as a coder. I have trained a
machine learning model on an imbalanced dataset. The
predictor variable is the column [Insert column name] .
In Python, how do I oversample and/or undersample my
data?
Prompt: I want you to act as a data scientist and explain
the model’s results. I have trained a decision tree model
and I would like to find the most important features. Please
write the code.
Prompt: I want you to act as a coder in Python. I have a
dataset [name] with columns [name] . [Describe
graph requirements]
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Index
16. 11.Visualize Image Grid Matplotlib
12. Explain Modelwith Lime
13. Explain Modelwith Shap
Prompt: I want you to act as a coder. I have a folder of
images. [Describe how files are organised in
directory] [Describe how you want images to be
printed]
Prompt: I want you to act as a data scientist and explain
the model’s results. I have trained a [library
name] model and I would like to explain the output using
LIME. Please write the code.
Prompt: I want you to act as a data scientist and explain
the model’s results. I have trained a scikit-learn XGBoost
model and I would like to explain the output using a series
of plots with Shap. Please write the code.
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17. 14.Write Multithreaded Functions
15. Compare Function Speed
16. Create NumPyArray
17.Write UnitTest
Credit: @svpino
Prompt: I want you to act as a coder. Can you help me
parallelize this code across threads in Python?
Prompt: I want you to act as a software developer. I would
like to compare the efficiency of two algorithms that
performs the same task in Python. Please write code that
helps me run an experiment that can be repeated for 5
times. Please output the runtime and other summary
statistics of the experiment. [Insert functions]
Prompt: I want you to act as a data scientist. I need to
create a numpy array. This numpy array should have the
shape of (x,y,z). Please initialize the numpy array with
random values.
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18. 18.Validate Column
EXPLAIN CODE
19. Explain Python
Credit: @svpino
Prompt: I want you to act as a software developer. Please
write unit tests for the function [Insert function] . The
test cases are: [Insert test cases]
Prompt: I want you to act as a data scientist. Please write
code to test if that my pandas Dataframe [insert
requirements here]
Prompt: I want you to act as a code explainer. What is this
code doing? [Insert code]
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19. 20. Explain SQL
21. Explain Google Sheets Formula
OPTIMIZE CODE
22. Improve Code Speed
Prompt: I want you to act as a data science instructor. Can
you please explain to me what this SQL code is
doing? [Insert SQL code]
Prompt: I want you to act as a Google Sheets formula
explainer. Explain the following Google Sheets
command. [Insert formula]
Prompt: I want you to act as a software developer. Please
help me improve the time complexity of the code
below. [Insert code]
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20. 23. Optimize Pandas
24. Optimize PandasAgain
25. Optimize Python
Prompt: I want you to act as a code optimizer. Can you
point out what’s wrong with the following pandas code and
optimize it? [Insert code here]
Prompt: I want you to act as a code optimizer. Can you
point out what’s wrong with the following pandas code and
optimize it? [Insert code here]
Prompt: I want you to act as a code optimizer. The code is
poorly written. How do I correct it? [Insert code here]
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21. 26. Optimize SQL
27. SimplifyPython
FORMATCODE
28.Write Documentation
Credit: @svpino
Prompt: I want you to act as a SQL code optimizer. The
following code is slow. Can you help me speed it
up? [Insert SQL]
Prompt: I want you to act as a code simplifier. Can you
simplify the following code?
Prompt: I want you to act as a software developer. Please
provide documentation for func1 below. [Insert
function]
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22. 29. Improve Readability
30. Format SQL
TRANSLATE CODE
31.Translate Between DBMS
32.Translate Pythonto R
Credit: @svpino
Prompt: I want you to act as a code analyzer. Can you
improve the following code for readability and
maintainability? [Insert code]
Prompt: I want you to act as a SQL formatter. Please
format the following SQL code. Please convert all reserved
keywords to uppercase [Insert
requirements] . [Insert Code]
Prompt: I want you to act as a coder and write SQL code
for MySQL. What is the equivalent of PostgreSQL’s
DATE_TRUNC for MySQL?
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23. 33.Translate Rto Python
Credit: @svpino
EXPLAIN CONCEPTS
34. Explainto Five-Year-Old
Prompt: I want you to act as a code translator. Can you
please convert the following code from Python to
R? [Insert code]
Prompt: I want you to act as a code translator. Can you
please convert the following code from R to
Python? [Insert code]
Prompt: I want you to act as a data science instructor.
Explain [concept] to a five-year-old.
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24. 35. Explainto Undergraduate
36. Explainto Professor
37. Explainto Business Stakeholder
Prompt: I want you to act as a data science instructor.
Explain [concept] to an undergraduate.
Prompt: I want you to act as a data science instructor.
Explain [concept] to a professor.
Prompt: I want you to act as a data science instructor.
Explain [concept] to a business stakeholder.
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25. 38. Explain Like Stackoverflow
SUGGESTIDEAS
39. Suggest Edge Cases
40. Suggest Dataset
Prompt: I want you to act as an answerer on
StackOverflow. You can provide code snippets, sample
tables and outputs to support your answer. [Insert
technical question]
Prompt: I want you to act as a software developer. Please
help me catch edge cases for this function [insert
function]
Prompt: I want you to act as a data science career coach. I
want to build a predictive model for [...] . At the same
time, I would like to showcase my knowledge in [...] .
Can you please suggest the five most relevant datasets for
my use case?
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26. 41. Suggest Portfolio Ideas
42. Suggest Resources
43. SuggestTime Complexity
Prompt: I want you to act as a data science coach. My
background is in [...] and I would like to [career
goal] . I need to build a portfolio of data science projects
that will help me land a role in [...] as a [...] . Can
you suggest five specific portfolio projects that will
showcase my expertise in [...] and are of relevance
to [company] ?
Prompt: I want you to act as a data science coach. I would
like to learn about [topic] . Please suggest 3 best
specific resources. You can include [specify resource
type]
Prompt: I want you to act as a software developer. Please
compare the time complexity of the two algorithms
below. [Insert two functions]
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27. 44. Suggest Feature Engineering
45. SuggestAbTesting Steps
46. CareerCoaching
Prompt: I want you to act as a data scientist and perform
feature engineering. I am working on a model that
predicts [insert feature name] . There are
columns: [Describe columns] . Can you suggest
features that we can engineer for this machine learning
problem?
Prompt: I want you to act as a statistician. [Describe
context] Please design an A/B test for this purpose.
Please include the concrete steps on which statistical test
I should run.
Prompt: I want you to act as a career advisor. I am looking
for a role as a [role name] . My background is [...] .
How do I land the role and with what resources exactly in 6
months?
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28. TROUBLESHOOTPROBLEM
47. Correct Own ChatGPTCode
48. Correct Python Code
49. Correct SQLCode
50.Troubleshoot PowerBI Model
Credit: Mathias Halkjær Petersen
Prompt: Your above code is wrong. [Point out what is
wrong] . Can you try again?
Prompt: I want you to act as a software developer. This
code is supposed to [expected function] . Please help
me debug this Python code that cannot be run. [Insert
function]
Prompt: I want you to act as a SQL code corrector. This
code does not run in [your DBMS, e.g. PostgreSQL] .
Can you correct it for me? [SQL code here]
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29. WRITE SQL
51. Create RunningAverage
52. Solve Leetcode Question
Credit: DataLemur
Prompt: I want you to act as a Power BI modeler. Here is
the details of my current project. [Insert details] . Do
you see any problems with the table?
Prompt: I want you to act as a data scientist and write SQL
code for me. I have a table with two columns [Insert
column names] . I would like to calculate a running
average for [which value] . What is the SQL code that
works for PostgreSQL 14?
Prompt: Assume you are given the tables… with the
columns… Output the following… [Question from Data
Lemur)
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30. WRITE OTHER CODE
53.Write Google Sheets Formula
54.Write R
55.Write Shell
Prompt: I want you to act as a bot that generates Google
Sheets formula. Please generate a formula
that [describe requirements]
Prompt: I want you to act as a data scientist using R. Can
you write an R script that [Insert requirement here]
Prompt: I want you to act as a Linux terminal expert.
Please write the code to [describe requirements]
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31. 56.WriteVBA
MISC
57. FormatTables
58. Summarize Book
Prompt: I want you to act as an Excel VBA developer. Can
you write a VBA that [Insert function here] ?
Prompt: I want you to act as a document formatter. Please
format the following into a nice table for me to place in
Google Docs? [insert text table here]
Prompt: I want you to act as a technical book summarizer.
Can you please summarize the book [name] with 5 main
points?
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32. 59. Summarize Paper
60. Provide Emotional Support
Source: https://github.com/travistangvh/ChatGPT-Data-Science-Prompts
Otheruseful ChatGPTpromptsforData
Science
1. Act as a data scientist and build a machine learning model for me. I
have a dataset of customer churn data. Please create a model that
predicts customer churn using features such as [insert metrics].
[Insert data set]
2. Act as an automatic machine learning (AutoML) bot using TPOT. I’m
working on a model that predicts credit card fraud. Provide Python
code to identify the optimal classification model, aiming for the
highest AUC score on the test dataset.
3. Can you help me train a [model name]? Please provide the Python
code to tune the hyperparameters and predict [parameters].
4. Please write code for [subject] data visualization and exploration,
including scatter plots, histograms, and correlation matrices.
Prompt: I want you to act as an academic. Please
summarise the paper [...] in simple terms in one
paragraph.
Prompt: I want you to provide emotional support to
me. [Explain problem here.]
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33. 5. I need a dataset with [number] rows and [number] columns. The
columns should include “age,” “gender,” “income,” and “purchase
history.” Please provide the dataset in CSV format.
6. Write a Python regex that matches email addresses. The regex
should account for variations in domain names and handle common
email formats.
7. Please build a machine-learning model that predicts [subject] based
on historical data. Use the past year as the training period and the
most recent month as the validation period.
8. I want you to act as a data scientist and perform feature engineering
for a customer churn prediction model. The dataset contains
customer [metrics]. Please write Python code to generate new
features that could improve the model’s performance.
9. Can you help me implement a natural language processing (NLP)
model? I have a dataset of customer reviews, and I want to classify
them into positive and negative sentiment categories. Please provide
the code for text preprocessing, feature extraction, and model
training.
10. I need a dataset for sentiment analysis with [number] rows and
[number] columns: “text” and “label” (positive/negative). Please
generate the dataset in CSV format.
11. Write a Python script to scrape data from a website. The script
should extract information such as [metrics/information] and save it
in a structured format like [formatting code language].
12. Act as a data scientist and build a recommendation system for an
ecommerce platform. The dataset contains user browsing and
purchase history. Please write in Python code.
13. Can you help me perform dimensionality reduction on a high-
dimensional dataset? Please write a structured query language
(SQL) code to apply principal component analysis (PCA) and
visualize the data in a reduced dimension space.
14. I have a dataset of customer transactions. Please write code to
calculate various customer lifetime value (CLV) metrics, such as
[metrics].
15. I want you to act as a programmer in [programming language].
Please simplify this code: [insert code].
Source: https://www.semrush.com/blog/chatgpt-prompts/#chatgpt-
prompts-for-data-science
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35. Home Prompts For AI Prompts For ChatGPT
83 Smart ChatGPTPrompts For
EducationAndTeachers
In the ever-evolving landscape of education, teachers are constantly
seeking innovative tools and techniques to enhance learning experiences
and engage students effectively. One such tool that has gained significant
traction in recent years is Smart ChatGPT prompts. These AI-powered
prompts offer educators a wealth of opportunities to augment their
teaching practices, foster creativity, and personalize instruction for
diverse learners. Let’s delve into how teachers can leverage Smart
ChatGPT prompts to elevate education and empower their students.
Understanding Smart ChatGPT Prompts for Education
Smart ChatGPT prompts are tailored cues or queries that prompt the AI
model to generate responses, suggestions, or solutions related to various
educational tasks and challenges. From lesson planning and content
creation to student engagement and assessment, these prompts serve as
invaluable aids for educators across different grade levels and subjects.
Enhancing Lesson Planning and Content Creation
PROMPTS FOR CHATGPT
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36. One of the primary ways teachers can leverage Smart ChatGPT prompts
is in lesson planning and content creation. By providing prompts such as
“Develop an interactive lesson on fractions for fifth-grade students” or
“Create engaging learning materials for teaching Shakespearean
literature,” educators can tap into ChatGPT’s vast knowledge base to
generate ideas, activities, and resources tailored to their specific teaching
objectives and student needs.
Personalizing Instruction and Differentiating Learning
Personalization and differentiation are key principles in modern
education, allowing teachers to meet the unique needs and learning styles
of individual students. Smart ChatGPT prompts can assist teachers in
personalizing instruction by generating adaptive learning activities,
remedial exercises, or enrichment materials based on students’
proficiency levels, interests, and preferences. For example, prompts like
“Design differentiated assignments for English language learners” or
“Suggest enrichment activities for advanced math students” can help
teachers tailor instruction to address diverse learning needs effectively.
Facilitating Student Engagement and Collaboration
Engaging students in meaningful learning experiences is essential for
fostering active participation and deepening understanding. Smart
ChatGPT prompts can spark creativity and ignite curiosity, encouraging
students to explore new concepts, brainstorm ideas, and collaborate with
their peers. Teachers can use prompts to generate discussion topics,
project ideas, or creative writing prompts that resonate with students’
interests and experiences, promoting authentic learning and collaboration
in the classroom.
Providing Timely Feedback and Assessment
Effective feedback and assessment play a crucial role in driving student
learning and growth. Smart ChatGPT prompts can support teachers in
providing timely, targeted feedback on student work, assessments, or
projects. By inputting prompts such as “Generate feedback for a student’s
essay on climate change” or “Assess student understanding of quadratic
equations,” educators can receive AI-generated responses that highlight
strengths, identify areas for improvement, and guide next steps in the
learning process.
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37. Tips for Effectively Using Smart ChatGPT Prompts
1. Start with Clear Objectives: Define your teaching objectives and
student learning outcomes before using Smart ChatGPT prompts to
ensure alignment with instructional goals.
2. Tailor Prompts to Student Needs: Customize prompts to address
specific learning needs, interests, and proficiency levels of your
students.
3. Promote Critical Thinking: Encourage students to critically evaluate
AI-generated responses and engage in reflective dialogue to deepen
their understanding.
4. Blend AI with Human Expertise: Use Smart ChatGPT prompts as a
supplemental tool alongside your expertise as an educator,
combining AI-driven insights with pedagogical knowledge and
experience.
Here arethe best ChatGPTPromptsfor
Education andTeachers
ChatGPTPrompts ForTeacherProductivity
1. List To Tables: “Turn this list into a table.”
2. Add Emojis: “Add emojis to this text for an email.”
3. Shorten Or Simplify Text: “Simplify this text passage for a [#] grade
audience.”
4. Highlight Key Information: “Underline key points of this passage.”
Table of Contents
■ Here are the best ChatGPT Prompts for Education and Teachers
■ ChatGPT Prompts For Teacher Productivity
■ ChatGPT Prompts For Lesson Planning
■ ChatGPT Prompts For Emails
■ Emails To Colleagues
■ Emails To Parents
■ Emails To Students
■ ChatGPT Prompts For Vocabulary
■ ChatGPT Prompts For Bellwork
■ Other useful ChatGPT prompts for Education and Teachers
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38. 5. Organize Info Into A List: “Organize the following information into a
list.”
ChatGPTPrompts ForLesson Planning
1. Historical Role-Play: “Imagine you are a key figure during the
Renaissance. Write a conversation between you and another
historical figure discussing your contributions to
art/science/philosophy.”
2. Science Mystery: “Write a short story where students follow clues
related to the scientific method to solve a mystery or uncover a
scientific discovery.”
3. Math Scavenger Hunt: “Create a list of math problems based on real-
life scenarios for a scavenger hunt activity. Each problem leads
students to the next clue.”
4. Interactive Geography Quiz: “Develop an interactive geography quiz
where students ask questions about countries, and ChatGPT
provides answers. Students guess the country and its features.”
5. Literary Character Interview: “Have students prepare questions as if
they were interviewing a character from a book. Use ChatGPT to
respond from the character’s perspective.”
6. Language Learning Simulation: “Design a simulation where students
can ‘travel’ to a foreign country and have conversations with locals in
the language they’re learning, with ChatGPT playing the role of the
local.”
7. Historical Newspaper Project: “Students write articles about key
historical events as if they were reporting in that period. Use
ChatGPT to provide additional context and quotes.”
8. Virtual Art Gallery Tour: “Create an art gallery showcasing famous
artworks. Students interact with ChatGPT as a curator, asking about
artists, styles, and historical context.”
9. Virtual Time Machine: “Build a ‘time machine’ activity where
students ask ChatGPT to transport them to a historical era. ChatGPT
describes the sights, sounds, and experiences of that time.”
10. Creative Writing Collaborative Story: “Initiate a collaborative story
where each student adds a sentence or paragraph to the narrative,
with ChatGPT guiding the story’s direction based on student input.”
Math AI Prompts:
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39. 1. “Create a lesson plan for teaching basic addition to PreK students.”
2. “Provide an interactive math activity to help Kindergarteners learn
to count.”
3. “Suggest a fun way to teach fractions to 3rd graders.”
ELA AI Prompts:
1. “Share a reading activity for improving phonics skills in 1st graders.”
2. “Give me a writing prompt for encouraging imaginative stories in 2nd
graders.”
3. “Suggest a lesson plan to introduce poetry to 4th graders.”
Science:
1. “Design a hands-on experiment for teaching the concept of magnets
to PreK students.”
2. “Provide a science lesson plan for exploring the life cycle of
butterflies with Kindergarteners.”
3. “Help me create a fun astronomy lesson for 5th graders.”
Social Studies:
1. “Suggest an activity to teach young children about community
helpers.”
2. “Create a lesson on American symbols suitable for 1st graders.”
3. “Help me plan a geography lesson for 4th graders using maps and
globes.”
Art:
1. “Share an art project idea that introduces PreK students to
basic shapes and colors.”
2. “Give me a creative assignment for teaching Kindergarteners about
famous artists.”
3. “Help me design an art lesson on making collages for 3rd graders.”
Physical Education:
1. “Suggest a fun and active game to improve motor skills in PreK
children.”
2. “Create an exercise routine for Kindergarten physical education
classes.”
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40. 3. “Provide a sports-related lesson plan for 2nd graders, emphasizing
teamwork.”
ChatGPTPrompts ForEmails
EmailsTo Colleagues
1. Professional Development Sharing: “Share insights from the recent
workshop/conference you attended and discuss how it can benefit
our teaching practices.”
2. Collaborative Project Proposal: “Propose a joint project or activity
that our classes can work on together, and outline the goals and
logistics.”
3. Lesson Plan Exchange: “Offer to exchange lesson plans or teaching
resources that have worked well in your classroom.”
4. Meeting Agenda and Reminder: “Outline the agenda for our
upcoming meeting and remind colleagues to review any materials
beforehand.”
5. Congratulatory Note: “Send a note congratulating a colleague on an
accomplishment or milestone, such as a publication or successful
event.”
EmailsTo Parents
1. Student Progress Update: “Provide an update on your student’s
academic progress, behavior, and recent achievements.”
2. Upcoming Event Details: “Share details about an upcoming school
event, field trip, or parent-teacher conference, including dates, times,
and expectations.”
3. Homework and Assignment Summary: “Summarize the week’s
homework assignments, upcoming tests, and any important due
dates.”
4. Request for Support: “Reach out to parents for volunteer support or
donations for a class project, event, or fundraiser.”
5. Positive Feedback: “Share a specific instance of your student’s
excellent performance or behavior in class to celebrate their
achievements.”
EmailsTo Students
1. Assignment Clarification: “Clarify instructions for an upcoming
assignment, providing step-by-step guidance and expectations.”
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41. 2. Study Tips and Resources: “Share study tips and additional
resources to help students prepare for an upcoming test or project.”
3. Encouragement and Motivation: “Send a motivational email to boost
students’ confidence and inspire them to reach their goals.”
4. Deadline Reminder: “Remind students about upcoming assignment
due dates, along with any tips for time management and staying
organized.”
5. Feedback and Improvement: “Provide constructive feedback on a
recent assignment and offer suggestions for improvement.”
ChatGPTPrompts ForVocabulary
1. Word Association Game: “Provide a word, and ask students to come
up with as many related words as possible. Use ChatGPT to validate
their responses and introduce new related words.”
2. Contextual Sentences: “Give students a list of vocabulary words.
Have them create sentences using these words, and then use
ChatGPT to review and provide feedback on the sentences.”
3. Synonym and Antonym Exploration: “Present a vocabulary word and
ask students to provide synonyms and antonyms. ChatGPT can help
verify their answers and suggest additional synonyms/antonyms.”
4. Descriptive Paragraphs: “Assign students a vocabulary word and
have them write descriptive paragraphs incorporating the word.
ChatGPT can provide examples of such paragraphs.”
5. Vocabulary Stories: “Challenge students to create short stories that
incorporate a set of vocabulary words. Use ChatGPT to help refine
the stories and expand on their vocabulary usage.”
6. Word Puzzles and Riddles: “Create vocabulary-based puzzles,
anagrams, or riddles. Students solve them and then discuss the
meaning of the words with the help of ChatGPT.”
7. Vocabulary in Real-Life Context: “Share news articles, excerpts from
literature, or real-world scenarios containing target vocabulary.
Discuss the context and meaning with ChatGPT.”
8. Word Exploration Webquest: “Assign students a vocabulary word
and have them research its origins, etymology, and various
meanings. ChatGPT can provide additional insights.”
9. Vocabulary Charades: “Choose vocabulary words and have students
act them out in a game of charades. ChatGPT can assist in
generating clues for the words.”
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42. 10. Vocabulary Journal: “Assign each student a vocabulary word to
explore throughout the week. Have them keep a vocabulary journal
with definitions, sentences, and synonyms.”
ChatGPTPrompts ForBellwork
1. Word of the Day: “Introduce a new word each day and ask students
to define it, use it in a sentence, and identify synonyms and
antonyms.”
2. Quick Math Challenge: “Present a math problem that reviews a
concept from the previous lesson. Students solve it within a time
limit.”
3. Inference Exercise: “Share a short paragraph with implicit
information. Ask students to make inferences about the characters,
setting, or events.”
4. Grammar Corrections: “Provide a sentence with grammatical errors.
Have students identify and correct the errors.”
5. Vocabulary Crossword Clue: “Give a list of clues for vocabulary
words. Students complete a mini crossword puzzle with the target
words.”
6. Quick Science Fact: “Share an intriguing science fact or trivia related
to the current unit of study. Ask students to reflect on its
significance.”
7. Geography Snapshot: “Display a map snippet of a specific location.
Students label major landmarks, cities, or geographical features.”
8. Historical Time Capsule: “Present a picture or excerpt from a
historical period. Students write a short paragraph imagining life
during that time.”
9. Creative Writing Prompt: “Provide the beginning of a story or a
writing prompt. Students continue the story or respond creatively.”
10. Question of the Day: “Pose a thought-provoking question about the
upcoming lesson. Students brainstorm ideas and share their
thoughts.”
Otheruseful ChatGPTpromptsfor
Education andTeachers
1. Develop a project-based learning activity for high school students
incorporating real-world problem-solving and critical thinking skills.
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43. 2. Create a study guide for [subject] that covers all
[topics/concepts/formulas].
3. Design a hands-on science experiment that demonstrates the
principles of [scientific concept].
4. Generate a list of 10 educational podcasts for students interested in
[topic/subject].
5. Provide a step-by-step tutorial on how to solve quadratic equations
using the quadratic formula.
6. Create a mind map that illustrates the interconnectedness of various
historical events during a specific period.
7. Suggest five strategies for promoting active learning in a large
classroom setting.
8. Design a group project for collaboration and problem-solving skills
for middle school students.
9. Create a set of [number] flashcards to help students memorize
vocabulary words in a foreign language.
10. Develop a rubric for assessing oral presentations in a public
speaking class.
11. Write a test on [subject] covering [key points]. [Insert supplemental
information]
12. Design an interactive online course module incorporating videos,
quizzes, and interactive lessons to engage learners.
13. Create a list of 10 educational YouTube channels that cover a wide
range of subjects for self-paced learning.
14. Develop a lesson plan on [topic]for high school students, covering
topics like [topics].
15. Write a quiz with five multiple-choice questions on [topic].
Other useful ChatGPT prompts:
240 Smart ChatGPT Prompts For Developers
102 Smart ChatGPT Prompts For Data Analysis
65 Smart ChatGPT Prompts For Ecommerce
46 Smart ChatGPT Prompts For Resume
80 Smart ChatGPT Prompts For Email Writing, Email marketing
43 Smart ChatGPT Prompts For Advertising
43 Smart ChatGPT Prompts For Content Creation and Social Media
80 Smart ChatGPT Prompts For Business
80 Smart ChatGPT Prompts For Marketing
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45. Home Prompts For AI Prompts For ChatGPT
60 Smart ChatGPTPrompts ForUI/UX
Design
In the realm of UI/UX design, harnessing the power of AI-driven tools like
ChatGPT can significantly streamline the creative process, boost
productivity, and elevate the overall quality of designs. These prompts
serve as invaluable assistants, offering inspiration, generating ideas, and
providing helpful nudges in the right direction. Let’s explore how
designers can effectively utilize ChatGPT prompts to enhance their UI/UX
endeavors.
Understanding ChatGPT Prompts for UI/UX Design
ChatGPT, an AI language model developed by OpenAI, can be leveraged to
provide prompts specifically tailored for UI/UX design tasks. These
prompts are essentially short, descriptive cues that guide designers in
generating design ideas, solving problems, or refining existing concepts.
Generating Design Ideas
PROMPTS FOR CHATGPT
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46. One of the most valuable aspects of ChatGPT prompts is their ability to
inspire fresh design ideas. By providing a brief description or problem
statement, designers can prompt ChatGPT to generate innovative
solutions or suggest creative directions for their projects. For example, a
prompt like “Design a user-friendly dashboard for a productivity app” can
yield a variety of design concepts and layout suggestions tailored to the
specific requirements.
Iterative Design Exploration
ChatGPT prompts can also facilitate iterative design exploration, allowing
designers to rapidly prototype and refine their ideas. Designers can input
prompts related to specific design elements, such as navigation menus,
color schemes, or typography choices, to receive feedback and
suggestions for improvement. This iterative process enables designers to
explore multiple design iterations quickly, leading to more refined and
polished UI/UX solutions.
Problem Solving and Troubleshooting
In UI/UX design, encountering challenges and roadblocks is inevitable.
ChatGPT prompts can be invaluable in helping designers overcome these
obstacles by offering alternative perspectives and problem-solving
strategies. Designers can input prompts describing the issues they’re
facing, such as “How can I improve the onboarding experience for new
users?” or “What are some ways to optimize the checkout process?”
ChatGPT can then provide insights, recommendations, and potential
solutions to address these challenges effectively.
Enhancing User Engagement and Accessibility
Ensuring a seamless user experience and addressing accessibility
concerns are paramount in UI/UX design. ChatGPT prompts can assist
designers in brainstorming ways to enhance user engagement and make
their designs more accessible to a diverse audience. For instance,
prompts like “Incorporate interactive elements to improve user
engagement” or “Optimize the design for users with visual impairments”
can prompt ChatGPT to generate design suggestions focused on these
objectives.
Tips for Effectively Using ChatGPT Prompts
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47. 1. Be Specific: Provide clear and concise prompts tailored to your
design objectives or challenges.
2. Experiment with Different Prompts: Explore a variety of prompts to
stimulate creativity and generate diverse design ideas.
3. Iterate and Refine: Use ChatGPT prompts iteratively to refine your
designs and address any issues or shortcomings.
4. Combine Human Creativity with AI Assistance: While ChatGPT can
offer valuable insights, remember to leverage your own creative
intuition and expertise in the design process.
Belowarethe best ChatGPTprompts
forUI/UX Design:
ChatGPTPromptsforUserExperience (UX)
1. I’m designing a [type of product] for [user persona]. I’ll give you a
description of a new feature I’m working on. Generate user flows for
that feature.
The feature: [quick description of the feature]
The possibilities:
2. Generate a wireframe layout for the following user flow [example of
the user flow]
3. Suggest a color palette for [type of product] targeting [user persona].
4. Suggest typography for [type of product] targeting [user persona].
5. I’m trying to [goal] in [application and platform description]. Give me
some [goal] tips.
6. I’m designing a [element] for [type of product]. Create a checklist of
what I should consider including.
7. I need to prepare [type of workshop] for [audience]. The goal is to
[workshop goal]. The timebox is [time limit]. Recommend a sample
agenda for the workshop.
Table of Contents
■ Below are the best ChatGPT prompts for UI/UX Design
■ ChatGPT Prompts for User Experience (UX)
■ Best ChatGPT prompts for UI design
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Index
48. 8. I’ll provide you with a product description and target buyer persona.
Please generate 10 examples of headlines for the landing page.
Product description: [1–2 sentences describing the value
proposition]
Target persona: [quick persona description]
9. Create a sample [type of content] [specific example] for [type of
product]. The [type of content] should be [desired length].
10. Please translate the text I’ll provide below to [desired language].
11. Recommend copywriting for [placement] that appears when
[context].
12. What are the most popular [type of product] in [market segment]?
13. Analyze the user experience of [web link]/[app store link]
14. What are the main trends in the [industry] in [market]?
15. I’m preparing a script for a user interview. The goal of the interview is
to [objective]. The product is [product category]. The target group is
[target segment]. Help me prepare a list of questions I could ask.
16. What’s the best way to [task you want to accomplish] in [design
tool]?
17. Give me examples of specific resources for learning [tool] for
someone who used [similar tool] in the past.
18. What are the best practices for designing [component]?
19. I’m a [role]. I received a feedback that [feedback]. Please recommend
relevant [next steps/courses/books/conferences/etc.] to improve in
the area.
20. Help me write a [tone] [type of message] to a client. I need to [brief
description of the message]
21. Summarize the [type of content] below, focusing on [area of focus].
22. Generate a list of [number] user interface (UI) design requirements
for a [product].
23. Develop a PDF typography style guide for a web application,
including font families, sizes, and usage guidelines.
24. What are the key UI considerations I should consider when designing
a navigation menu for an ecommerce website?
25. Generate a sample usability report for a competitor’s social media
platform by analyzing this [user feedback] and identifying areas for
improvement.
26. As a UX designer, what gamification techniques can I incorporate
into a [app type] app?
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49. 27. How can I design a [industry] website in a way that conveys
professionalism to potential clients?
28. What are some engaging micro-interactions to consider when
designing a language learning mobile app?
29. Create an interactive wireframe prototype for a [industry] platform
using a design tool of your choice.
30. Develop a user persona for a [app type] app targeting millennials and
Gen Zs.
31. What are some best practices for designing accessible forms in web
applications to ensure inclusivity for users with disabilities?
32. Create a text-based Excel sheet to collect and organize user
feedback from usability testing sessions. Assume a team of
[number] UX researchers.
33. How can I optimize the onboarding experience for a shopping app to
increase user engagement and reduce drop-off rates?
34. Generate user flows for a [app type] app, covering scenarios such as
[topics].
35. Generate survey questions to understand user preferences and
needs for a new [messaging platform] for [remote teams].
36. Create a responsive checklist for [charts].
37. Help us outline the Who, Why, What, and How of our new fitness app.
We aim to address specific user pain points and would like to ensure
that our design aligns with these factors.
38. Generate a set of user interview questions aimed at understanding
the user experience of an e-commerce website. Focus on elements
like ease of navigation, search functionality, and checkout.
39. Recommend tools designed for creating high-fidelity prototypes.
Include tools that offer real-time collaboration features and are easy
to learn for beginners.
40. Please create detailed user personas for a music-streaming app.
Consider factors like age, location, musical preferences, and usage
habits. Also, focus on how to increase user engagement.
41. Recommend color schemes appropriate for a health and wellness
app. Consider factors like user mood, trustworthiness, and
readability.
42. Analyze this wireframe for our mobile travel booking app and
suggest improvements for a smoother user flow. Focus on elements
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50. like menu placement, button sizes, and the arrangement of form
fields.
43. Could you explain the term ‘Information Architecture’ in a way that
anyone can understand without using jargon? Use an example to
explain thoroughly.
44. I am designing an app for a recruitment agency that wants to
evaluate potential candidates by testing their skills and knowledge.
Write down key design requirements for this app
45. Can you generate 3 personas for a blockchain game (similar to Gods
Unchained)?
46. I am designing a blockchain trading card game for players that love
Greek mythology and want to own their digital content via the use of
NFTs. Create a user flow for the potential players who want to play
the game.
47. I am designing an app that helps users record, transcribe, and
timestamp important parts from virtual meetings so that users can
spend less time in unnecessary calls, and also increase the amount
of data gained from user interviews. Can you list the competitors for
this kind of app and perform a competitive analysis?
48. Create a user journey map in a tabular format for a chef using a
recipe app.
49. Can you list down 5 design ideas for a graphic design tool (like
Canva)?
50. I am designing a travel blog that will be monetized through the
combination of SEO and affiliate marketing. Write down some visual
guidelines for this website
Best ChatGPTpromptsforUI design
1. Design a user-friendly checkout process for an e-commerce website
with an emphasis on reducing cart abandonment.
2. Generate ideas for interactive elements that encourage user
engagement in a fitness-tracking app.
3. Suggest color schemes and typography for a finance management
app that conveys trust and reliability.
4. Write copy for a modal telling users about a website’s cookies policy.
Write it in an approachable tone.
5. Create a user flow for onboarding in a language learning app.
Emphasize the ease of use for beginners.
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51. 6. Design a feedback form that encourages users to provide detailed
insights for a product improvement app.
7. Generate layout concepts for a minimalist portfolio website that
effectively showcases a photographer’s work.
8. Suggest UI elements for a clean and modern dashboard to display
sales analytics.
9. Suggest microinteraction ideas to make a social media feed more
engaging without overwhelming the user.
10. Create a checklist for UI elements for a perfect [screen/feature] in a
table
Other useful ChatGPT prompts:
240 Smart ChatGPT Prompts For Developers
102 Smart ChatGPT Prompts For Data Analysis
65 Smart ChatGPT Prompts For Ecommerce
46 Smart ChatGPT Prompts For Resume
80 Smart ChatGPT Prompts For Email Writing, Email marketing
43 Smart ChatGPT Prompts For Advertising
43 Smart ChatGPT Prompts For Content Creation and Social Media
80 Smart ChatGPT Prompts For Business
80 Smart ChatGPT Prompts For Marketing
21 Smart ChatGPT prompts for SEO
46 Smart ChatGPT Prompts For Sales
98 Smart ChatGPT Prompts For Customer Service
Conclusion
Incorporating ChatGPT prompts into the UI/UX design workflow can be a
game-changer for designers looking to streamline their processes,
generate innovative ideas, and overcome design challenges effectively. By
leveraging the power of AI-driven prompts, designers can enhance the
user experience, improve accessibility, and deliver exceptional design
solutions that resonate with users. With the right approach and mindset,
ChatGPT becomes an indispensable tool in the arsenal of every UI/UX
designer, empowering them to create intuitive, visually appealing, and
user-centric digital experiences.
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Index
53. Home Prompts For AI Prompts For ChatGPT
240 Smart ChatGPTPrompts For
Developers
In the ever-evolving landscape of software development, creativity,
problem-solving, and efficient coding practices are paramount. As
developers strive to create innovative solutions and streamline workflows,
they often face challenges in generating ideas, troubleshooting issues,
and optimizing code. Smart ChatGPT prompts offer a revolutionary
approach to address these challenges by providing developers with AI-
generated suggestions, insights, and solutions. Let’s explore how
developers can effectively leverage ChatGPT prompts to enhance their
productivity and creativity in various aspects of software development.
1. Idea Generation:
Developers can use ChatGPT prompts to brainstorm new project
ideas, features, or enhancements for existing applications.
By providing a brief description of their project goals and
requirements, developers can prompt ChatGPT to generate
innovative suggestions and concepts to inspire their creativity.
PROMPTS FOR CHATGPT
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54. 2. Code Optimization:
ChatGPT prompts can assist developers in optimizing their code for
performance, readability, and maintainability.
Developers can input snippets of code or describe specific
programming challenges, and ChatGPT can provide
recommendations for improving efficiency, identifying potential
bugs, or refactoring code for better structure.
3. Troubleshooting and Debugging:
When faced with errors or bugs in their code, developers can turn to
ChatGPT prompts for troubleshooting assistance.
By describing the symptoms of the issue and providing relevant code
snippets, developers can prompt ChatGPT to suggest possible
causes and solutions, helping them quickly identify and resolve
issues.
4. Documentation and Technical Writing:
Writing comprehensive documentation and technical guides is
essential for ensuring the usability and scalability of software
projects.
Developers can use ChatGPT prompts to generate outlines,
summaries, or explanations for complex technical concepts, making
it easier to create clear and concise documentation for their
projects.
5. Learning and Skill Development:
ChatGPT prompts can serve as valuable learning tools for
developers looking to expand their knowledge and skills in specific
programming languages, frameworks, or technologies.
Developers can prompt ChatGPT to generate tutorials, practice
exercises, or explanations of advanced concepts, helping them
deepen their understanding and expertise in their chosen field.
Table of Contents
■ Below are the best ChatGPT prompts for Developers
■ ChatGPT Prompts for Writing Code
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55. ■ ChatGPT Prompts for Code Review and Debugging
■ ChatGPT Prompts for Code Explanation
■ ChatGPT Prompts for Optimizing Code
■ ChatGPT Prompts for Code Improvements
■ ChatGPT Prompts for Learning New Concepts
■ ChatGPT Prompts for Design Patterns
■ ChatGPT Prompts for Syntax Help
■ ChatGPT Prompts for Code Refactoring
■ ChatGPT Prompts for Brainstorming Ideas
■ ChatGPT Prompts for Interview Preparation
■ ChatGPT Prompts for Learning and Using APIs
■ ChatGPT Prompts for Understanding Error Messages
■ ChatGPT Prompts for Project Management & Agile Methodologies
■ ChatGPT Prompts for Regular Expressions
■ Other useful ChatGPT prompts for Developers
■ A multi-prompt approach (prompt chaining)
■ 1. Modernize and add best practices
■ 2. Review your code for logical errors and security concerns
■ 3. Validate the recommendations (reflexion)
■ 4. Write the Code
■ 5. Create Tests
■ Re-write Prompt
■ ChatGPT prompt optimizer
■ Ask for alternatives
■ Documentation / Explaination
■ Adding Documentation
■ Write your terms and conditions
■ Produce cheat sheets
■ Generate Readme Files
■ Write detailed blogs
■ Explain Code
■ Architecture Diagram (Mermaid)
■ Entity Relationship Diagram (Mermaid)
■ Code Refactoring
■ Refactor Code
■ Modernizing Old Code
■ Code in to Multiple Methods
■ Better Performance
■ Adding a Parameter to a Function
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56. Belowarethe best ChatGPTprompts
forDevelopers:
ChatGPTPromptsforWriting Code
1. Write a function in [language] to calculate the [mathematical
concept].
2. Create a [language] function to [perform task].
3. Write a [language] program that [performs task] using [library or
algorithm].
■ Adding Coding Best Practices or Principles
■ Follow coding style guidelines
■ Detecting and Fixing Errors
■ Create Unit Tests
■ Transpiling Code
■ Responsive Design
■ Internationalization
■ Add comments to code
■ Code Generation
■ Create Functions
■ Generate a Dockerfile
■ Write a RegEx
■ Create a Class
■ Add Functionality
■ You are a world class software engineer
■ Code Review
■ Error Hendling
■ Suggest Improvements
■ Product Service Promotion
■ Generate innovative product ideas
■ Develop a unique value proposition
■ Master the art of storytelling for marketing
■ Create a successful referral program
■ Master the art of upselling and cross-selling
■ Create a viral marketing campaign
■ Develop a powerful elevator pitch
■ Create an actionable marketing plan
■ Leverage content marketing for lead generation
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57. 4. Write a [language] script that reads from [data source] and outputs
to [data destination].
5. Can you generate a [language] code that implements [data structure
or algorithm]?
6. Can you provide a [language] script to parse [file format]?
7. Implement a [language] function to handle [task].
8. Create a [language] script to sort an array of [data type].
9. Write a [language] function to find the [n-th element] in a [data
structure].
10. Implement a [language] program that reads [input] and writes
[output].
11. Show me how to write a [language] function that performs [specific
task].
12. How do I create a class in [language] with these attributes:
[attributes list]?
13. Write a [language] script to connect to a database and perform
[database operation].
14. Provide a [language] code to perform file operations like [file
operations list].
15. Create a [language] function that converts [data type A] into [data
type B].
16. Write a [language] script that interacts with [database] and performs
[operations].
17. Generate a [language] class to model a [real-world object] with these
properties:
.
18. Create a [language] function to [perform task] with the following
inputs: [input variables].
19. Write a [language] script to connect to [database] and execute
[operation].
20. Can you generate a [language] class for [object] with these
attributes:
?
21. Write a [language] script to process [data type] and achieve [task]
with these requirements: [requirements list].
22. Develop a [language] function to perform [task] using [methodology
or library] with the inputs: [input variables].
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58. 23. Can you help me code a [language] algorithm to solve [problem]
given the constraints: [constraints list]?
24. Could you create a [language] function to [task], which takes in [input
variables] and returns [output], under these constraints [constraints
list]?
25. Write a [language] script to parse [file format], extract [information],
and store the data in [data structure] following these requirements:
[requirements list].
26. Write a [language] function to calculate [mathematical concept]
using [algorithm]. The function should take these inputs: [input
variables] and return [expected output].
27. Develop a [language] program to read [file type], perform [operations]
and write the results to [output format].
28. Create a [language] program that reads [input file type], performs
[operations], then writes the results to [output file type] following the
format: [format description].
29. Implement a [language] script that uses [API] to retrieve [data type]
and store it in [database].
30. Write a [language] function named [function name] that performs
[task]. The function should accept these inputs: [input variables] and
return [expected output]. Also, handle the following edge cases:
[edge case description].
31. Implement a [language] script using [library/API] that retrieves [data
type], performs [operation], and then stores it in [database] with a
structure of [database schema].
32. Implement a [language] algorithm for [task], given these input
parameters [input parameters], it should output [expected output]
and consider these constraints [constraints list].
33. Please write a [language] function named [function name] to [task],
which takes in [input variables] and returns [output], under these
constraints [constraints list].
34. Create a [language] script to parse [file format], extract [information],
and store the data in [data structure] with the following
requirements: [requirements list].
35. Implement a [language] algorithm for [task], given these input
parameters [input parameters], it should output [expected output]
and consider these constraints [constraints list].
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59. ChatGPTPromptsforCode Reviewand
Debugging
1. Can you identify any bugs in this [language] code snippet: [code
snippet]?
2. Review the given [language] code for potential scalability issues:
[code snippet].
3. Could you find potential issues in this [language] code: [code
snippet]?
4. Review this [language] function for errors: [code snippet].
5. Can you find any performance issues in this [language] code: [code
snippet]?
6. Are there any security vulnerabilities in this [language] code: [code
snippet]?
7. Can you spot any potential problems with this [language] class
definition: [code snippet]?
8. Can you analyze this [language] code: [code snippet] and point out
potential errors?
9. Look over this [language] script: [code snippet]. Are there any bugs?
10. Please review this [language] code for style and best practices: [code
snippet].
11. Do you see any memory leaks in this [language] code: [code
snippet]?
12. Can you review this [language] function: [code snippet] and suggest
areas for error handling?
13. I am concerned about security issues in this [language] code: [code
snippet]. What are your thoughts?
14. Review the following [language] function: [code snippet] and provide
suggestions for error handling and potential bottlenecks.
15. Help identify any potential security issues in the following [language]
code: [code snippet] related to [specific vulnerability].
16. Help me understand why this [language] function is not working as
expected: [code snippet].
17. What are the potential issues with this [language] recursive function:
[code snippet]?
18. Can you help me debug this error message from my [language]
program: [error message]?
19. Find any potential issues in this [language] code that processes
[data type]: [code snippet].
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60. 20. Can you spot the bug in this [language] function that handles [task]:
[code snippet]?
21. What’s wrong with this [language] method for [task]: [code snippet]?
22. Could you review this [language] code that performs [task] and
identify potential bugs or issues: [code snippet]?
23. Help me debug this [language] script that processes [data type] and
suggest possible fixes: [code snippet].
24. Find the memory leaks in the following [language] code and suggest
possible optimizations: [code snippet].
25. Please review this [language] code that is supposed to [task] given
the inputs [input variables] and return [output]: [code snippet].
26. Find potential bugs in the [language] script that processes [data
type] and outputs [output type]: [code snippet].
27. Identify the logic error in this [language] function intended to [task]
with these inputs: [input parameters] and expected output: [output
description].
28. Please review the following [language] code that is supposed to
[task] given the inputs [input variables], return [output] and follows
these coding guidelines: [coding guidelines]: [code snippet].
29. Identify and fix potential bugs in the [language] script that processes
[data type], uses these resources [resources list], and outputs [output
type]: [code snippet].
30. Find the logic error in this [language] function that is intended to
[task], given these inputs: [input parameters], and expected to
produce [output description], but currently gives [incorrect output].
31. Debug the given [language] code: [code snippet]. It should perform
[expected behavior], but it’s producing [current behavior].
32. Review the following [language] function named [function name]:
[code snippet]. Please identify any potential bugs, performance
issues, and non-compliance with [coding standard].
33. Debug the following [language] code: [code snippet]. It’s expected to
perform [expected behavior] but instead, it’s producing [current
behavior] when given inputs: [input examples].
34. Please review the [language] function: [code snippet] for any
potential memory leaks or performance issues when processing
[data type] of size [data size].
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61. ChatGPTPromptsforCode Explanation
1. Can you explain what this [language] function does: [code snippet]?
2. I’m having trouble understanding this [language] class. Can you
explain it: [code snippet]?
3. Could you break down this [language] loop and explain what it does:
[code snippet]?
4. Could you break down how this [language] function works: [code
snippet]?
5. What does this [language] recursive function do: [code snippet]?
6. Help me understand what this [language] code snippet does: [code
snippet].
7. Could you explain the logic behind this [language] function: [code
snippet]?
8. Can you explain this [language] algorithm implementation: [code
snippet]?
9. Help me understand the workings of this [language] data structure
implementation: [code snippet].
10. Explain this [language] code that uses lambda functions: [code
snippet].
11. Can you help me understand this [language] script: [code snippet]?
12. Explain what this [language] function does: [code snippet].
13. What does this section of the [language] code do: [code snippet]?
14. Can you walk me through the flow of this [language] script: [code
snippet]?
15. Please explain what the following block of [language] code does:
[code snippet] and how it interacts with [system components].
16. Can you explain the functionality of this [language] algorithm: [code
snippet] and its expected output for given inputs: [input examples]?
17. Break down this [language] class: [code snippet], and explain how its
methods accomplish [task].
18. Could you explain how this [language] function: [code snippet]
works? Especially, how it uses [specific feature] to accomplish
[task]?
19. I’m struggling to understand the following block of [language] code:
[code snippet]. Could you break it down for me, especially the part
where it implements [algorithm or feature]?
20. Can you explain how this [language] code: [code snippet]
accomplishes [task] and why it uses [specific method or feature]?
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62. ChatGPTPromptsforOptimizing Code
1. Suggest improvements to optimize this [language] function: [code
snippet].
2. Can you provide a more efficient version of this [language] algorithm:
[code snippet]?
3. How can I improve the performance of this [language] script: [code
snippet]?
4. This [language] function: [code snippet] is running slower than I’d
like. Any optimization suggestions?
5. I need to improve the speed of this [language] algorithm: [code
snippet]. What changes would you recommend?
6. How could I make this [language] data processing code more
efficient: [code snippet]?
7. The following [language] function: [code snippet] runs slower than
expected when processing [input type]. Any suggestions for
optimization?
8. How can I improve the performance of this [language] function:
[code snippet] when handling [large dataset]?
9. Provide optimization suggestions for the following [language] code:
[code snippet] used to process [data type].
10. How can I optimize this [language] function: [code snippet] to
perform [task] more quickly when handling [large data size] and
maintain accuracy of [accuracy requirement]?
11. I have this [language] function: [code snippet]. It works as expected
but runs slower than I’d like when handling [specific data]. Any
suggestions for performance improvement?
12. The following [language] code: [code snippet] performs [task].
However, it seems inefficient with [data type] of size [data size]. How
can I optimize it?
ChatGPTPromptsforCode Improvements
1. How can I make this [language] function more efficient: [code
snippet]?
2. Suggest alternative methods or functions to improve this [language]
code: [code snippet].
3. What are some improvements I can make to this [language]
algorithm: [code snippet]?
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63. 4. Can you suggest ways to make this [language] function more
readable: [code snippet]?
5. This [language] class: [code snippet] seems a bit convoluted. Any
ideas for simplification?
6. How could I refactor this [language] code to use more modern
features: [code snippet]?
7. Suggest ways to refactor this [language] function: [code snippet] to
improve readability and maintainability.
8. Can you provide alternative approaches to perform [task] for the
given [language] code: [code snippet]?
9. Provide recommendations to make this [language] code: [code
snippet] more idiomatic and efficient.
10. Please review this [language] function: [code snippet]. How can it be
made more readable, efficient, and compliant with [specific coding
standard]?
11. Could you suggest improvements to this [language] code: [code
snippet] to better handle [specific scenario] and follow the [best
practice]?
12. How can this [language] function: [code snippet] be refactored to
improve readability, performance, and compatibility with
[library/framework/API]?
13. Can you suggest code improvements for this [language] function
named [function name] that accomplishes [task] with these libraries
[libraries list], inputs: [input variables], and expected output: [output]:
[code snippet]?
ChatGPTPromptsforLearning NewConcepts
1. Explain the concept of [programming concept] in [language].
2. Could you explain how [library or framework] works in [language]?
3. I need help understanding the [programming paradigm] paradigm in
[language].
4. Can you explain the differences between [concept A] and [concept B]
in [language]?
5. What is the significance of [concept] when coding in [language]?
6. How does [concept] impact the way we write [language] code?
7. Explain how [concept] is used in [language] and provide a simple
code example that uses [specific features].
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64. 8. What are the key differences between [concept A] and [concept B] in
[language] and how do they affect code performance?
9. Can you explain [concept] in [language] and show me how it can be
used in a real-world application such as [use case]?
10. Can you explain the concept of [concept] in [language]? Also, could
you provide a code example that uses it to perform [task] with
[specific data type]?
11. I’m trying to understand [concept] in [language]. Could you explain it
with a practical example, especially in the context of [use case]?
12. Explain the difference between [concept A] and [concept B] in
[language], their performance implications, and use-cases where one
would be preferable over the other.
ChatGPTPromptsforDesign Patterns
1. What is the best way to implement the [design pattern] pattern in
[language]?
2. Can you explain how the [design pattern] pattern works in
[language]?
3. How do I use the [design pattern] design pattern in this [language]
code: [code snippet]?
4. Could you provide an example of using the [design pattern] pattern in
a [language] project?
5. How could I apply the [design pattern] pattern to this [language]
code: [code snippet]?
6. What are the benefits of using the [design pattern] pattern in a
[language] application?
7. Provide an example of how to implement the [design pattern] in
[language] for a software component that handles [task].
8. What is the best way to apply the [design pattern] in [language] to
solve [problem] in the given code: [code snippet]?
9. Explain how the [design pattern] can be used to improve the
following [language] code: [code snippet] used in [context].
10. Could you show me how to implement the [design pattern] in
[language] for a [specific system component] while considering
[specific constraints]?
11. Explain how to apply the [design pattern] in [language] to solve
[specific problem] in the context of [application type]. Also, provide a
code example.
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65. 12. I have this [language] code: [code snippet]. How could I refactor it to
follow the [design pattern] and improve [specific aspect]?
ChatGPTPromptsforSyntax Help
1. What is the correct syntax to [perform task] in [language]?
2. How do I use [command or function] in [language]?
3. Can you show me the syntax to [perform task] in [language]?
4. What’s the correct syntax for implementing [concept] in [language]?
5. How can I use the [language feature] in [language]?
6. I’m having trouble with the syntax for [concept] in [language]. Can
you help?
7. What is the correct syntax for performing [task] using [specific
feature] in [language]?
8. I’m struggling with the syntax for [concept] in [language]. Can you
show me how to do it with a code example?
9. How do I use the [specific syntax] in [language] to perform
[operation] on [data type]?
10. I’m unsure about the correct syntax for [operation] using [specific
feature] in [language]. Could you provide an example where it’s used
in [context]?
11. Could you show me the proper syntax and usage of [feature] in
[language] to accomplish [task] with [specific data type]?
12. I need help with the syntax for [concept] in [language]. Can you show
me a code snippet that uses it to solve [problem]?
ChatGPTPromptsforCode Refactoring
1. Suggest a refactor for this [language] function: [code snippet].
2. How can I make this [language] code more readable: [code snippet]?
3. What are some ways I can refactor this [language] script for better
performance: [code snippet]?
4. I’d like to refactor this [language] code to be more object-oriented:
[code snippet]. Any suggestions?
5. Could you show me how to refactor this [language] function to be
more idiomatic: [code snippet]?
6. I’m considering refactoring this [language] script to use [concept or
feature]: [code snippet]. How would you approach this?
7. How can I refactor the following [language] code: [code snippet] to
follow the [specific coding principle or pattern]?
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66. 8. Could you show me how to refactor this [language] function: [code
snippet] to use more modern features such as [specific feature]?
9. Suggest a way to refactor the following [language] code: [code
snippet] to improve [specific aspect].
10. How can I refactor this [language] code: [code snippet] to improve
[aspect] and align with [specific coding standard or principle]?
11. I want to refactor this [language] function: [code snippet] to make it
more idiomatic and maintainable. Additionally, it should handle
[specific edge cases]. Any suggestions?
12. Suggest a way to refactor this [language] code: [code snippet] to
follow [specific design pattern] while improving [specific aspect].
ChatGPTPromptsforBrainstorming Ideas
1. Can you suggest a few ideas for a [language] project involving
[technology or concept]?
2. What are some interesting features I could add to my [language]
application?
3. I need ideas for [language] functions to add to my [project type]
project.
4. Can you suggest some functionalities that I could add to my [type of
software] using [language]?
5. I’m working on a [language] project related to [domain]. What are
some interesting features I could implement?
6. I’m creating a [language] application for [use case]. What modules or
functionalities would be useful?
7. I’m starting a [language] project related to [domain]. What are some
key features that could make it stand out?
8. I’m creating a [language] application to solve [problem]. Can you
suggest some unique functionalities or design ideas?
9. How can I use [language] to implement innovative features in a [type
of software] project?
10. I’m planning to develop a [language] application in the [domain].
What are some innovative features I can implement considering the
latest [industry trend]?
11. I need to create a [language] project to solve [problem]. Can you help
me brainstorm some design ideas and potential challenges
considering [specific context]?
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67. ChatGPTPromptsforInterviewPreparation
1. Can you provide some common [language] interview questions?
2. I’m preparing for an interview. Could you give me some tricky
[language] questions and their solutions?
3. What are some challenging [language] tasks I might be asked to
code during an interview?
4. What are some common [language] problems asked in coding
interviews related to [concept]?
5. Can you provide some example tasks in [language] that are
commonly used to test [concept] in interviews?
6. I have an interview coming up for a [language] position focusing on
[specific topic]. Could you provide me with some common interview
questions and solutions?
7. What are some challenging problems in [language] related to
[concept] that are often asked in technical interviews?
8. I’m preparing for an interview that requires knowledge of [language]
and [concept]. Could you provide me some practice questions?
9. I’m preparing for a coding interview in [language]. Could you give me
an example of a common question about [topic], its optimal solution,
and an explanation of its complexity?
10. For my upcoming [language] coding interview, could you provide a
complex problem about [topic], its step-by-step solution, and an
analysis of its time and space complexity?
11. I am preparing for a [language] coding interview. Can you give me a
problem related to [topic], a sample optimal solution, and a brief
walkthrough of how it works?
ChatGPTPromptsforLearning and UsingAPIs
1. Can you provide an example of using the [API] in [language]?
2. Explain how to use the [specific endpoint] in the [API] using
[language].
3. How do I authenticate and make a request to [API] in [language]?
4. Can you show me how to interact with the [API] using [language] to
achieve [specific task]?
5. How would I go about making a [type of request] to [API] using
[language]?
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68. 6. I’m trying to use the [API] in my [language] project. Can you help me
understand how to use [specific endpoint]?
7. I’m trying to use the [API] in my [language] project. How can I use it
to perform [task]?
8. Can you show me how to use [specific endpoint] from the [API] using
[language] to achieve [specific result]?
9. How do I authenticate and make a [type of request] to [API] using
[language]?
10. I’m learning the [API name] in [language]. Can you explain how to use
the [specific endpoint/method] with [specific parameters] to perform
[task]?
11. Could you give me an example of how to use the [API name] in
[language] to retrieve [data type], filter with [criteria], and handle
[specific error]?
ChatGPTPromptsforUnderstanding Error
Messages
1. I’m getting this error message when running my [language] code:
[error message]. What does it mean?
2. Can you explain this [language] compiler error: [error message]?
3. I don’t understand this [language] runtime error: [error message]. Can
you help?
4. I got this error message in my [language] code: [error message].
What could be causing it?
5. I don’t understand what this [language] error message means: [error
message]. Can you explain it to me?
6. While running my [language] code, I encountered this error: [error
message]. How can I resolve it?
7. I encountered this error message when running my [language] code:
[error message]. What does it mean and how can I fix it?
8. I received the following error message when trying to implement
[task] in [language]: [error message]. Can you explain what’s going
wrong?
9. Help me understand this error message from my [language] code:
[error message] and suggest possible solutions.
10. I’m getting this error message: [error message] when I try to run my
[language] code: [code snippet]. What does it mean and how can I fix
it?
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69. 11. Could you explain the meaning of this [language] error message:
[error message] that occurs when executing the function: [code
snippet] using [input data]?
12. I encountered this error message: [error message] while working
with [language] on [task]. Could you explain what’s causing it and
suggest a way to resolve it?
ChatGPTPromptsforProject Management &
Agile Methodologies
1. Can you explain the principles of Agile in the context of a [type of
project] project?
2. What are the best practices for managing a [type of project] project
using Agile methodologies?
3. Can you explain how to apply Agile methodologies in a [language]
project with [specific conditions]?
4. What project management best practices should I consider for my
[language] project on [platform or domain]?
5. How can I apply Agile principles to manage my [language]
development project with a team of [team size]?
6. What are the benefits of using Scrum in a [language] project
developed for [industry/domain]?
7. I’m leading a [language] project with [team size] developers. Can you
provide best practices for managing the project using Agile
methodologies?
8. Can you explain how to implement the [Agile methodology] in a
[project type] with a team size of [number] and during [specific
constraints]?
9. I’m managing a [project type] using the [Agile methodology]. Could
you provide guidance on how to handle [specific challenge]
considering our team size of [number] and [specific condition]?
10. How can I adapt [Agile methodology] for a [project type] to achieve
[specific goal] while dealing with [specific challenge] and within
[specific timeframe]?
ChatGPTPromptsforRegularExpressions
1. Explain this regular expression in [language]: [regex].
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70. 2. Can you help me create a regular expression in [language] that
matches [pattern]?
3. How do I use regular expressions to [perform task] in [language]?
4. Can you help me create a regular expression in [language] to extract
[specific pattern] from [type of text]?
5. How can I use a regular expression in [language] to replace [specific
pattern] in [type of text]?
6. I need a regular expression in [language] that matches [pattern] in a
[context]. Can you help me construct one?
7. Can you help me understand this regular expression in [language]:
[regex]? It is supposed to match [pattern].
8. I want to use a regular expression in [language] to replace [pattern A]
with [pattern B] in [text or code snippet]. Can you help?
9. How do I create a regular expression in [language] that matches
[pattern] in a [data type] and handles [specific edge case]?
10. I need to write a regular expression in [language] to parse [specific
pattern] from [data type]. Can you guide me on that and also explain
how it works?
11. Can you explain how to write a regular expression in [language] to
extract [specific pattern] from [data type], considering [specific
scenario]?
Source refer: https://www.learnprompt.org/chat-gpt-prompts-for-coding/
Otheruseful ChatGPTpromptsfor
Developers
Amulti-prompt approach (prompt
chaining)
can be used to update, refactor, and review a piece of code. A well-
designed set of prompts is one where each has separated concerns and
singular responsibilities.
1. Modernize and add best practices
by getting GPT-4 to re-write your code into the style you want. This step
will generally result in coherent output, in the style you want, but may
introduce errors, so we do it first.
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71. Prompt:
Review the following code and re-write it to modern es6
programming standards and formatting:
[insert code here]
2. Reviewyourcodeforlogical errors and
securityconcerns
Get recommendations to improve any logical or security concerns
introduced. It’s important that we don’t ask for a refactor, just the
reasoning behind wanting the refactor.
Prompt:
Review your provided code 'tempFunction' for any logical or
security concerns and provide a list of recommendations.
3.Validatethe recommendations (reflexion)
Validate the provided recommendations. Reflexion is a powerful
technique to improve the accuracy of the initial recommendations and try
to eliminate any hallucinations. This is not always required but it is worth
asking if you are unsure about any recommendations.
Prompt:
Review your above recommendations. Tell me why you were
wrong and if any recommendations were overlooked or
incorrectly added?
4.Writethe Code
Combine your reviews, recommendations and feedback to get GPT-4 to
write your new function.
Prompt:
Re-write the 'tempFunction' function based off your review
and recommendations.
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72. 5. CreateTests
Create some simple tests that we can run locally and validate the results
Prompt:
Create two [ define technology ] tests for the above
'tempFunction' function. One that is expected to pass and
one that is expected to fail.
Re-write Prompt
Let’s see if we can get GPT4 to make or average prompts and turn them
into “voyage inspirant” type mastery prompts.
Prompt:
[your prompt]
Re-write the above text to be more verbose and include a lot of
superfluous description about each thing, use very painting language.
ChatGPTprompt optimizer
Prompt:
I'll provide a chatGPT prompt. You'll ask questions to
understand the audience and goals, then optimize the prompt
for effectiveness and relevance using the principle of
specificity.
Askforalternatives
If you’re not satisfied with your solution you can ask to ChatGPT to give
you alternatives
Prompt:
I'll provide you with a piece of code that I made and
I need you give me alternatives to do the same in other way:
[INSERT YOUR CODE HERE]
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73. Documentation / Explaination
Adding Documentation
Note
Adding documentation requires creating clear and comprehensive
explanations of a module’s purpose, design, and implementation.
Prompt 1#:
I don't know how to code, but I want to understand how this
works. Explain the following code to me in a way that a non-
technical person can understand. Always use Markdown with
nice formatting to make it easier to follow. Organize it by
sections with headers. Include references to the code as
markdown code blocks in each section. The code:
[insert code here]
Prompt 2#:
Please add comprehensive documentation for [file or module
name], including clear and concise explanations of its
purpose, design, and implementation. Consider including
examples of how to use the module, as well as any relevant
diagrams or flow charts to help illustrate its workings.
Ensure that the documentation is easily accessible to other
developers and is updated as the module evolves. Consider
using documentation tools such as inline comments, markdown
files, or a documentation generator to simplify the process.
[insert code here]
Writeyourterms and conditions
Prompt:
Create terms and services for my website about an [AI tool]
called [name].
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74. Produce cheat sheets
Prompt:
Write a cheat sheet for [markdown formatting].
Generate Readme Files
Prompt:
Generate documentation for the code below. You should
include detailed instructions to allow a developer to run it
on a local machine, explain what the code does, and list
vulnerabilities that exist in this code.
[enter code]
Write detailed blogs
Prompt:
Write a detailed blog on How to build a [COVID tracker]
using React with proper structuring of code.
Explain Code
Note
Don’t spend time trying to figure out how code works, just ask ChatGPT to
explain it to you
Prompt:
Context: I'm starting a new position as backend developer
and I have to start to understand how some functions are
working
Technologies: [INSERT YOUR TECHNOLOGIES HERE]
You have to: explain me the code line by line
[INSERT YOUR CODE HERE]
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75. Architecture Diagram (Mermaid)
Note
Create a diagram of your architecture using Mermaid
Prompt:
Write the Mermaid code for an architecture diagram for this
solution [DESCRIBE SOLUTION]
EntityRelationship Diagram (Mermaid)
Note
Create an entity relationship diagram using Mermaid
Prompt:
Write the Mermaid code for an entity relationship diagram
for these classes [INSERT CLASSES]
Code Refactoring
RefactorCode
Note
Ask to ChatGPT to refactor your code
Prompt:
I have a piece of code and I need you do a refactor of it:
[INSERT YOUR CODE HERE]
Refactoring code is an essential process in software development that
aims to improve the quality, readability, and maintainability of existing
code without altering its functionality. Refactoring can enhance code
efficiency, reduce errors, and make it easier to modify or extend in the
future. With ChatGPT’s help, you can effectively refactor your code and
achieve a better code structure.
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76. Modernizing Old Code
Note
By providing your old function to GPT-4 and asking it to refactor it to
modern coding practices, you can quickly modernize your code.
Prompt:
Refactor the following code to modern es6 programming
standards:
[INSERT YOUR CODE HERE]
Code into Multiple Methods
Note
If you have a long function that is doing too much, you can ask GPT-4 to
refactor it into multiple methods.
Prompt:
Refactor the following code into multiple methods to improve
readability and maintainability:
[INSERT YOUR CODE HERE]
BetterPerformance
Note
If you have a function that is taking too long to run, you can ask GPT-4 to
refactor it to improve performance.
Prompt:
Refactor the following code to improve performance:
[INSERT YOUR CODE HERE]
Adding a Parameterto a Function
Prompt:
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