You're reading from Unlocking Data with Generative AI and RAG Enhance generative AI systems by integrating internal data with large language models using RAG

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781835887905

Length 346 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Keith Bourne

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Table of Contents (20) Chapters

Preface

1. Part 1 – Introduction to Retrieval-Augmented Generation (RAG) FREE CHAPTER

2. Chapter 1: What Is Retrieval-Augmented Generation (RAG)

3. Chapter 2: Code Lab – An Entire RAG Pipeline

4. Chapter 3: Practical Applications of RAG

5. Chapter 4: Components of a RAG System

6. Chapter 5: Managing Security in RAG Applications

7. Part 2 – Components of RAG

8. Chapter 6: Interfacing with RAG and Gradio

9. Chapter 7: The Key Role Vectors and Vector Stores Play in RAG

10. Chapter 8: Similarity Searching with Vectors

11. Chapter 9: Evaluating RAG Quantitatively and with Visualizations

12. Chapter 10: Key RAG Components in LangChain

13. Chapter 11: Using LangChain to Get More from RAG

14. Part 3 – Implementing Advanced RAG

15. Chapter 12: Combining RAG with the Power of AI Agents and LangGraph

16. Chapter 13: Using Prompt Engineering to Improve RAG Efforts

17. Chapter 14: Advanced RAG-Related Techniques for Improving Results

18. Index

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Other advanced RAG techniques to explore

As with just about everything else we have discussed with RAG and GenAI, the options available for advanced techniques to apply to your RAG application are too numerous to list or even keep track of. We have selected techniques that are focused on aspects of RAG specifically, categorizing them based on the areas of your RAG application they will likely have the most impact.

Let’s walk through them in the same order that our RAG pipelines operate, starting with indexing.

Indexing improvements

These are advanced RAG techniques that focus on the indexing stage of the RAG pipeline:

Deep chunking: The quality of retrieved results often depends on the way your data is chunked before it’s stored in the retrieval system itself. With deep chunking, you use DL models, including transformers, for optimal and intelligent chunking.
Training and utilizing embedding adapters: Embedding adapters are lightweight modules trained...