INTRO TO
Andreea Turcu
Head of Global Training @H2O.ai
H2O.ai Confidential
Table of Contents
1. Opening Remarks
2. Demystifying RAG (Retrieval Augmented
Generation)
3. Exploring the H2O Generative AI Ecosystem
4. Showcasing Enterprise h2oGPTe
5. Wrapping Up: Conclusion and Questions &
Answers
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Opening Remarks
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LLMs vs. Generative AI
Generative AI
Large
Language
Models
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Benefits of Generative AI
1. Content Creation
2. Creative Assistance
3. Natural Language Understanding
4. Personalization
5. Data Augmentation
6. Automation of Repetitive Tasks
7. Language Translation
etc.
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Challenges of Generative AI
1. Bias in Data
2. Lack of Control
3. Security Concerns & Data Privacy
4. Resource Intensiveness
5. Understanding Context
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Demystifying RAG
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Source: snorkel.ai/which-is-better-retrieval-augmentation-rag-or-fine-tuning-both
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Exploring the H2O
Generative AI
Ecosystem
v
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H2O Gen AI
A truly open-source generative AI, giving
organizations the power to create your
own large language models while
maintaining your data integrity.
A framework and no-code GUI
designed for fine-tuning
state-of-the-art large language
models complete with enterprise
support.
Vector Database
Index and store documents in a
Vector database for efficient
search. This provides relevant
context to a question in real
time, allowing LLMs to create
concise and domain specific
responses.
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H2O AI and GenAI Ecosystem
Documents
Data Sources
LLM
DataStudio
myGPT
LLM
EvalStudio
Vector DB
(Embeddings
)
Alternative
Datasets
Query + Documents
(Context)
Talk to Your
Data
● Ques Answers
● Context Search
● Doc Retrieval
● Similar Doc
● Personalization
Contextual Similarity
Continuous Eval
(feedback)
Gen AI App Store
+ +
+ +
+
+
Datasets AI Engines AI Apps
LLM
Integration
Models
Data to QA pairs
ETL for LLMs
LLM Fine Tuning
Custom GPT
API
End User
Enterprise
v
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Enterprise h2oGPTe
● AI-powered search to answer questions about
documents, websites, and workplace content
● Document question-answering platform
○ Search engine
○ Question-answering powered by open source
H2OGPT models
○ Smart data ingest, smart indexing, smart retrieval
○ Supports air-gapped installations
● Integrate with H2O’s Document AI technology for
extracting insights and creating value from documents
across the enterprise using ICR, NLP, and computer
vision
Search & chat made fast, fun, and easy
v
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Enterprise h2oGPTe - Python API
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Showcasing
Entreprise h2oGPTe
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Wrapping Up
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Greg Fousas Data Scientist @H2O.ai
Laurene De Peyrelongue Data Scientist @H2O.ai
AN IN-DEPTH EXPLORATION OF
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Table of Contents
1. End-to-end pipeline
2. h2oGPTe UI
3. h2oGPTe Python API
4. WAVE app
v
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End-to-end Pipeline
Productionise the
functionality with the Python
API
Create an WAVE app and
publish it in the h2o platform
to be used by the business
Use the h2oGPTe UI to
develop the solution to your
task
v
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Enterprise h2oGPTe
● AI-powered search to answer questions about
documents, websites, and workplace content
● Document question-answering platform
○ Search engine
○ Question-answering powered by open source
H2OGPT models
○ Smart data ingest, smart indexing, smart retrieval
○ Supports air-gapped installations
● Integrate with H2O’s Document AI technology for
extracting insights and creating value from documents
across the enterprise using ICR, NLP, and computer
vision
Search & chat made fast, fun, and easy
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h2oGPTe
Try it at https://genai.h2o.ai
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h2oGPTe Python API
Productionize the h2oGPTe functionality by using Python code
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WAVE
Publish the solution created in the previous steps with a WAVE app
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Wrapping Up
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Thank you!

Enterprise h2o GPTe Learning Path Slide Deck