Generative AI with RAG and VectorDB
(C) Copyright Elephant Scale
April 14, 2024
- Generative AI with Large Language Models (LLM) opens ways to building smart applications as never before.
- The most popular architecture for that is Retrieval-Augmented Generation (RAG.) RAG systems are built with semantic search.
- In this course, the students learn how build the RAG systems.
- For semantic component we use VectorDB from DataStax.
- For the Generative AI, we have a choice of LLMs, such as ChatGPT or local LLama for HIPAA compliance.
- For the implementation, we teach the best cloud and cloud architecture for your project.
- Talk to an LLM in a correct way.
- Script talking to LLM for a programmatic implementation.
- Organize your private documents for the implementation and break them into meaningful fragments for storing in the semantic search engine (VectorDB or Pinecone.)
- Structure the flow of conversation with LLM about your private documents.
- Implement the system in production.
- Architect testing, and continuous improvements.
- Developers, data scientists, team leads, project managers
- Intermediate to advanced.
- Three days
- Can be broken into introduction and advanced parts of appropriate length
- General familiarity with machine learning
- Lectures and hands on labs. (50% – 50%)
- Zero Install: There is no need to install software on students’ machines!
- A lab environment in the cloud will be provided for students.
- A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly.
- A checklist to verify connectivity will be provided
- Chrome browser
- Introduction to AI and LLM
- Iterative development
- How to iteratively analyze and refine your prompts to generate marketing copy from a product fact sheet.
- Summarizing
- How to make an LLM summarize a document with different requirements and in different formats
- Inferring
- How to make an LLM infer sentiment and topics from product reviews and news articles.
- Transforming
- How to use Large Language Models for text transformation tasks such as language translation, spelling and grammar checking, tone adjustment, and format conversion.
- Expanding
- How to generate customer service emails that are tailored to each customer’s review.
- Chatbot
- How to use an LLM to have extended conversations with chatbots personalized or specialized for specific tasks or behaviors.
- Organize your private documents for the implementation and break them into meaningful fragments for storing in the semantic search engine (VectorDB or Pinecone)
- Semantic search
- Retrieval Augmented Generation (RAG)
- Recommender systems
- Hybrid search
- Facial similarity search
- Anomaly detection
- Models, prompts, and parsers
- Memory
- Chains
- Q&A
- Evaluation
- Conversational bot
- Overview of Amazon, Azure, and Google clouds of RAG
- Evaluating and debugging Generative AI
- Practical examples and demos