Unlocking Data with Generative AI and RAG

Table of Contents

1. What Is Retrieval-Augmented Generation (RAG)
2. Code Lab – An Entire RAG Pipeline
3. Practical Applications of RAG
4. Components of a RAG System
5. Managing Security in RAG Applications
6. Interfacing with RAG and Gradio
7. The Key Role Vectors and Vector Stores Play in RAG
8. Similarity Searching with Vectors
9. Evaluating RAG Quantitatively and with Visualizations
10. Key RAG Components in LangChain
11. Using LangChain to Get More from RAG
12. Combining RAG with the Power of AI Agents and LangGraph
13. Using Prompt Engineering to Improve RAG Efforts
14. Advanced RAG-Related Techniques for Improving Results