ChatGPT is an advanced AI language model developed by OpenAI, capable of generating human-like text responses through natural language processing. It builds on the Generative Pre-trained Transformer (GPT) architecture, with various versions (GPT-1 to GPT-4) enhancing capabilities like context understanding, content generation, and ethical considerations. Key applications include integration with established brands for tasks like content creation, customer support, and workflow management across multiple industries.

2. TOC
➔ What is Chat GPT?
➔ What is ‘GPT’ in Chat GPT?
➔ GPT Models
➔ How Chat GPT Works?
➔ Summarizing Chat GPT
➔ Applications of Chat GPT (in
established Brands)
➔ Applications of ChatGPT (in general
Market)
➔ How to integrate Chat GPT with Apps?
➔ Other Open AI Products

3. What is ChatGPT?
ChatGPT is an advanced AI language model developed by
OpenAI. It represents a significant leap in natural language
processing, enabling AI to generate contextually relevant,
and almost human speech-like text responses in a
conversational manner i.e. It can understand and generate
human-like text responses to a wide variety of questions and
prompts.
It is designed to converse with humans and provide helpful
information, assistance, or entertainment.

4. What is ‘GPT’ in ChatGPT?
Generative: GPT models
are capable of
generating new content
based on the patterns
and context they have
learned from the
training data. They can
create human-like text
that is contextually
relevant and coherent.
Transformer: GPT models are built
on the Transformer architecture, a
neural network model designed for
natural language processing tasks.
The Transformer architecture
employs self-attention mechanisms
and parallel processing to efficiently
handle large-scale language tasks
and generate contextually accurate
text.
Pre-trained: The models are
pre-trained on vast amounts of
text data from diverse sources,
allowing them to learn a wide
range of linguistic patterns,
grammar, facts, and context.
This pre-training process forms
the foundation for their ability
to generate high-quality text.

5. GPT MODELS
GPT 1 | June 2018
GPT 2 | Feb 2019
GPT 3 | June 2020
GPT 3.5 | March 2022
GPT 4 | March 2023

6. GPT 1
Pros:
➔ The strengths of GPT-1 was its ability to generate
fluent and coherent language when given a
prompt or context.
➔ The use of these diverse datasets allowed GPT-1
to develop strong language modeling abilities
Cons:
➔ Generated repetitive text, especially when given
prompts outside the scope of its training data.
➔ It failed to reason over multiple turns of dialogue
and could not track long-term dependencies in
text.
➔ Its cohesion and fluency were only limited to
shorter text sequences, and longer passages
would lack cohesion.
Number of Parameters:117
Million
Training Data: Common
Crawl, Book Corpus
Maximum Sequence Length:
1024

7. GPT 2
Pros:
➔ GPT-2 has the ability to generate coherent and
realistic sequences of text.
➔ GPT 2 could generate human-like responses,
making it a valuable tool for various natural
language processing tasks, such as content
creation and translation.
Cons:
➔ GPT-2 struggled with tasks that required more
complex reasoning and understanding of
context.
➔ While GPT-2 excelled at short paragraphs and
snippets of text, it failed to maintain context
and coherence over longer passages
Number of Parameters: 1.5
Billion
Training Data: Common
Crawl, Book Corpus,
Webtext
Maximum Sequence
Length:2048

8. GPT 3
Pros:
➔ Ability to generate coherent text, write computer
code, and even create art.
➔ GPT-3 understands the context of a given text and
can generate appropriate responses.
➔ The ability to produce natural-sounding text has
huge implications for applications like chatbots,
content creation, and language translation.
Cons:
➔ The model can return biased, inaccurate, or
inappropriate responses.
➔ The model still has difficulty understanding context
and background knowledge.
➔ Ethical implications and potential misuse of
powerful language models. used for malicious
purposes, like generating fake news, phishing emails,
and malware.
Number of Parameters: 175
Billion
Training Data: Common
Crawl, BookCorpus,
Wikipedia, Books, Articles
etc
Maximum Sequence
Length: 4096

9. GPT 3.5
Pros:
➔ GPT-3.5 includes new features and capabilities,
such as better support for long-form text and
improved language translation.
➔ Lower computational requirements: Despite
having a larger parameter count than GPT-3,
GPT-3.5 is more efficient in terms of
computational requirements, making it easier to
use and scale.
➔ Better Training Model
Cons
➔ Heavily on the quality and diversity of its training
data
➔ Ethical implications and potential misuse of
powerful language models. used for malicious
purposes, like generating fake news, phishing
emails, and malware.
Number of Parameters: 6.7
Billion
Training Data: Common
Crawl, BookCorpus,
Wikipedia, Books, Articles
languages, domains, and
styles
Maximum Sequence
Length: 4096

10. GPT 4 | Pros
➔ GPT-4 is even better at understanding and
producing various dialects and responding to
the text’s emotions.
➔ GPT-4 can work with dialects, which are
cultural or regional variations of a language.
➔ GPT-4 cites the sources it used when creating
content, making it easier for readers to verify
the information accuracy.
➔ GPT-4 goes one step further by producing
stories, poems, or essays with improved
coherence and creativity.
➔ GPT-4 is a powerful tool for education and
content creation because, for instance, it can
describe the content of a photo, identify
trends in a graph, and even generate captions
for images.
Number of Parameters: 1
Trillion

11. How Chat GPT Works?
Step 3:
Reinforcement
Learning Model
Step 1:
Supervised Fine
Tuning Model
Step 2:
Reward Model

12. Step 1 | Supervised Fine Tune Modeling
➔ The first development involved fine-tuning the GPT-3 model by hiring 40
contractors to create a supervised training dataset, in which the input has
a known output for the model to learn from.
➔ Inputs, or prompts, were collected from actual user entries into the Open
API.
➔ The labelers then wrote an appropriate response to the prompt thus
creating a known output for each input.
➔ The GPT-3 model was then fine-tuned using this new, supervised dataset,
to create GPT-3.5, also called the SFT model

13. Step 2 | Reward Model
➔ To train the reward model, labelers are presented with 4 to 9 SFT
model outputs for a single input prompt.
➔ They are asked to rank these outputs from best to worst, creating
combinations of output ranking.
➔ The next refinement comes in the form of training a reward model in
which a model input is a series of prompts and responses, and the
output is a scaler value, called a reward.

14. Step 3 | Reinforcement Learning Model
➔ The model is presented with a random prompt and returns a response. The
response is generated using the ‘policy’ that the model has learned in step 2.
➔ The policy represents a strategy that the machine has learned to use to
achieve its goal; in this case, maximizing its reward.
➔ Based on the reward model developed in step 2, a scaler reward value is then
determined for the prompt and response pair.
➔ The reward then feeds back into the model to evolve the policy.
➔ Using a KL penalty reduces the distance that the responses can be from the
SFT model outputs trained in step 1 to avoid over-optimizing the reward
model and deviating too drastically from the human intention dataset.

15. Evaluation Model
➔ Evaluation of the model is performed by setting aside a test set during training
that the model has not seen. On the test set, a series of evaluations are
conducted to determine if the model is better aligned than its predecessor,
GPT-3.
◆ Helpfulness: the model’s ability to infer and follow user instructions.
◆ Truthfulness: the model’s tendency for hallucinations..
◆ Harmlessness: the model’s ability to avoid inappropriate, derogatory, and
denigrating content.

16. Summarizing ChatGPT
➔ ChatGPT, which stands for Chat
Generative Pre-trained
Transformer.
➔ ChatGPT is built on what is called
an LLM (Large Language Model).
➔ Current version of ChatGPT is
based on the GPT-3.5 LLM and
GPT-4 LLM.
➔ GPT are a family of large language models
(LLMs), GPT models are artificial neural
networks that are based on the transformer
architecture(NLP).
➔ The model behind ChatGPT was trained on all
sorts of web content including websites,
books, social media, news articles, and more —
all fine-tuned in the language model by both
supervised learning and RLHF (Reinforcement
Learning From Human Feedback).

17. APPLICATIONS
OF CHAT GPT With
Established
Brands.

18. Stripe
➔ Developers can post natural language
queries within Stripe Docs to GPT-4,
which will answer by summarizing the
relevant parts of the documentation or
extracting specific pieces of
information. This lets developers spend
less time reading and more time
building.
➔ Stripe is working with OpenAI to
implement solutions for fraud
detection and increase conversion
rates.

19. Duolingo
Duolingo turned to
OpenAI’s GPT-4 to
advance the product
with two new
features: Role Play,
an AI conversation
partner, and Explain
my Answer, which
breaks down the
rules when you make
a mistake, in a new
subscription tier
called Duolingo Max.

20. Be My Eyes
➔ AI-powered visual assistance for
instantaneous image-to-text
generation.
➔ The Virtual Volunteer feature will
be integrated into the existing
app and contains a dynamic new
image-to-text generator powered
by OpenAI's GPT-4.
➔ Users can send images via the
app to an AI-powered Virtual
Volunteer, which will provide
instantaneous identification,
interpretation and conversational
visual assistance for a wide
variety of tasks

21. Slack
ChatGPT is coming to Slack.
Salesforce unveiled the news that
everyone’s favorite office messaging
software will be getting an
AI-powered assistant named Einstein
that can draft replies, summarize
threads, or do external research
without leaving Slack.

22. Khan Academy
Khan Academy uses
GPT-4 to power
Khanmigo, an AI-powered
assistant that functions as
both a virtual tutor for
students and a classroom
assistant for teachers.

23. Inworld AI
➔ Inworld is setting a new standard
for AI characters by powering the
“brains” that inspire their
personalities, dialogue, and
reactions. Using GPT-3, Inworld is
making this next generation of
characters more engaging.
➔ By leveraging GPT-3 as one of 20
machine learning models, Inworld
was able to build out
differentiated aspects of
characters’ personalities including
emotions, memory, and behaviors.

24. ➔ Wealth Manager- Morgan Stanley
➔ Yabble- Survey Management and
sentiment analysis of feedbacks
➔ Iceland - Using AI to preserve their
language and culture
➔ GitHub Copilot
➔ Windows Office 365
Some More Integrations

25. APPLICATIONS
OF CHAT GPT
In General
Market.

26. Content Generation
★ Content Creation
ChatGPT can be used to generate content for
blogs, articles, ads, marketing materials,
product descriptions, etc. It can assist with
research, generate topic ideas, and even write
content in a specific style or tone of voice.
Top industries to benefit: media, marketing,
publishing, education.
★ Summarization
It can provide automatic summarization of long
articles or documents, which can be useful for
people who need to quickly understand the main
points of a text without having to read through
the entire document.
Top industries to benefit: legal, media, education
★ Speech Recognition
The bot can transcribe spoken words into
text, making it easier for businesses to
analyze customer conversations.
Top industries to benefit: media, healthcare,
legal
★ Translation
You can use the bot to translate text in real
time for messaging apps, social media
platforms, and other communication channels.
Top industries to benefit: hospitality, travel,
media.

27. Workflow Management
★ Task Management
The bot can provide virtual assistants for task
management, including scheduling, reminders,
and to-do lists.
Top industries to benefit: marketing, finance, IT.
★ Email Management
It can help users sort, prioritize, and respond to
emails, improving productivity and reducing
email overload.
Top industries to benefit: eCommerce, business &
professional services.
★ Social Media Management
ChatGPT can help users schedule posts, respond
to comments and messages, and provide
recommendations for content and engagement
strategies.
Top industries to benefit: marketing and
advertising, eCommerce, media, entertainment.
★ Knowledge Management
You can use the bot to manage knowledge and
information, such as FAQ pages or employee
manuals, making it easier for employees and
customers to find answers to their questions.
Top industries to benefit: legal, healthcare,
finance, education.

28. Customer Experience & Interaction
★ Customer Support
The bot can answer questions, troubleshoot
technical issues, and provide info about
products and services.
Top industries to benefit: eCommerce,
healthcare, finance, logistics, Internet of Things,
fitness.
★ Personalization
ChatGPT can help personalize user experiences
by providing recommendations for products,
services, and content based on user preferences
and behavior.
Top industries to benefit: eCommerce, media,
entertainment, healthcare, finance, fitness.
★ Sales Assistance
You can use the bot to assist customers with
their purchase decisions, such as through
product comparison tools or chatbots that
provide product information and reviews.
Top industries to benefit: eCommerce,
healthcare, finance.
★ Customer Feedback Analysis
It can analyze customer feedback, such as
through sentiment analysis tools or chatbots
that collect feedback and provide actionable
insights.
Top industries to benefit: eCommerce,
marketing, healthcare.

29. Security & Compliance
★ Cybersecurity
The bot can monitor networks for suspicious
activity and alert security personnel to potential
threats. It also can analyze network traffic and
detect anomalies, such as unusual login attempts
or data transfers.
Top industries to benefit: IT, security, IoT, legal,
finance.
★ Compliance Monitering
You can use the bot to monitor compliance with
industry regulations or internal policies,
helping businesses avoid legal or ethical issues.
Top industries to benefit: legal, healthcare,
finance.
★ Risk Assessment
It can help businesses identify and assess potential
risks, and prepare for potential disruptions. For
example, it can analyze data from various sources,
such as network traffic logs, to identify potential
cyber threats that could impact a business.
Top industries to benefit: IT, finance, logistics,
healthcare.
★ Fraud Detection
ChatGPT can detect fraud by analyzing large
volumes of data, identifying patterns and
anomalies that may indicate fraudulent
activity.
Top industries to benefit: banking, finance,
eCommerce, healthcare

30. WorkFlow Optimization
★ Data Analysis
The bot can analyze data from various
sources, including databases, spreadsheets,
and social media platforms, to provide
insights on consumer behavior, market
trends, and other relevant information. The
virtual assistant can also help with data
visualization, creating charts and graphs to
make complex data more accessible to
businesses.
Top industries to benefit: eCommerce,
analytics, consulting, finance.
★ Decision-Making Support
ChatGPT can use machine learning algorithms
and natural language processing to analyze
vast amounts of data and provide insights
that can aid in decision-making. For example,
a virtual assistant powered by ChatGPT can
analyze market trends, financial statements,
and other data to provide investment
recommendations and help investors make
informed decisions.
Top industries to benefit: IT, finance,
eCommerce, healthcare.

31. WorkFlow Optimization
★ Finance Optimization
It can help with budgeting, bill payment, and
financial planning, and provide
recommendations for investment strategies
and opportunities.
Top industries to benefit: finance, banking,
eCommerce, retail.
★ Supply Chain Optimization
The bot can use natural language processing
and machine learning algorithms to track
inventory levels, monitor order fulfillment,
and manage other aspects of the supply chain.
Top industries to benefit: logistics,
manufacturing

32. HOW TO INTEGRATE
CHATGPT WITH APPS?

33. Ways to Integrate ChatGPT
The basic 3 Approaches for ChatGPT
integration.
➔ API Integration
➔ Using a chatbot builder platform
➔ Custom Implementation.

34. API Integration
It provides fewer customization options
compared to other methods. Therefore,
you won’t be able to fine-tune ChatGPT
to your needs but rather create an
interface within your app so users can
directly ask the bot.

35. ➔ Using a chatbot builder platform is an easy
and accessible way to integrate ChatGPT into
your mobile or web app. Such platforms
usually come with a variety of pre-built tools
and interfaces for creating chatbots,
including integrations with ChatGPT.
➔ Some of the chatbot builders that support
ChatGPT integrations are:
● Chatfuel
● Landbot
● Tars.
➔ These are subscription based systems.
Using a chatbot builder platform

36. Custom Implementation
➔ The process of creating a custom implementation
for ChatGPT integration involves:
◆ Defining the chatbot’s functionality
◆ Designing the conversation flow
◆ Creating the front-end interface
◆ And building the back-end logic to interface
with the ChatGPT API.
➔ The below can be used to create custom
implementation:
◆ Strategy 1. Fine-Tune ChatGPT Against Your
Dataset
◆ Strategy 2. Prompt Engineering with Your
Database

37. ➔ Fine Tuning
◆ This involves training the large language model (LLM) on data specific
to your domain. With ChatGPT, you can only fine-tune GPT-2 and
GPT-3 against custom data. OpenAI provides API access to download
links for different-sized models, which can be found in their respective
repositories.
◆ Once you have downloaded the model, you then need to use
TensorFlow, PyTorch or some other relevant library first to define the
training parameters and train the model against 80% of your data,
using 10% of your data for validation and another 10% for testing.
Custom Implementation Strategies

38. ➔ Prompt Engineering
◆ In this method, you store all your relevant company data in one single database.
Then, when a user puts in a prompt, you match it against your company data in the
database, find similar results to the user prompt, modify the prompt and send it
over to GPT-4 (or GPT3 if you are still on the waitlist).
◆ Using a database to store and query your custom data can be a very efficient way to
use that data for ChatGPT. This is because databases are designed to store and
query large amounts of data quickly. In addition, databases can be used to store
data in various formats, which means that you can store your custom data in the
most convenient format.
◆ 8000 to 32000 Tokens
Custom Implementation Strategies

39. ➔ Ask for specific Business use case.
➔ Get clarification on what is the type of
integration the client is looking for Web
or App?
➔ Ask for the Data set and resources along
with sample prompts and answers.
➔ Ask for Data formats (in which format
they have data)? Easiest implementations
are with database? Complex ones are all
excel , and Doc formats.
Important Queries to Ask

40. OTHER
IMPLEMENTATIONS
OF OPENAI ➔ Whisper
➔ DALL-E2

42. ➔ Computational Resources : GPU’s
➔ Licensing Fees : Open AI Product
Pricing https://openai.com/pricing
➔ Cloud Storage
Pricing to Use OpenAI Tools

43. Whisper
➔ ChatGPT and Whisper models are now
available on our API, giving developers
access to cutting-edge language (not just
chat!) and speech-to-text capabilities.
➔ EXAMPLE : Shop When shoppers search
for products, the shopping assistant makes
personalized recommendations based on
their requests.

44. DALL E 2, Stable Diffusion , Midjourney
➔ DALL-E 2 is a state-of-the-art neural network model developed by
OpenAI that can generate high-quality images from textual descriptions.
➔ Stable Diffusion is a deep learning, text-to-image model released in
2022. It is primarily used to generate detailed images conditioned on
text descriptions, though it can also be applied to other tasks such as
inpainting, outpainting, and generating image-to-image translations
guided by a text prompt.
➔ Midjourney is a generative artificial intelligence program and service
created and hosted by a San Francisco-based independent research lab
Midjourney, Inc. Midjourney generates images from natural language
descriptions, called "prompts".
➔ Examples: Character Creation , Image Editors and Creators

45. Ongoing Development
➔ Speed Documentation
➔ Sentence Completions
➔ Custom
Recommendations
Engines

46. Open Areas - Need Your Help
➔ Deployment Needs
➔ Process and Stages of Development
➔ Tech Stacks
➔ Tools that can be used

47. Thank You