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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19. Other Books You May Enjoy

Summary

In this chapter, we explored the key role that evaluation plays in building and maintaining RAG pipelines. We discussed how evaluation helps developers identify areas for improvement, optimize system performance, and measure the impact of modifications throughout the development process. We also highlighted the importance of evaluating the system after deployment to ensure ongoing effectiveness, reliability, and performance.

We introduced standardized evaluation frameworks for various components of a RAG pipeline, such as embedding models, vector stores, vector search, and LLMs. These frameworks provide valuable benchmarks for comparing the performance of different models and components. We emphasized the significance of ground-truth data in RAG evaluation and discussed methods for obtaining or generating the ground truth, including human annotation, expert knowledge, crowdsourcing, and synthetic ground-truth generation.

The chapter included a hands-on code lab where...

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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

Table of Contents (20) Chapters

Summary

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Table of Contents (20) Chapters

Summary

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