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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Evaluation helps you get better

Why is evaluation so important? Put simply, if you don’t measure where you are at, and then measure again after you have made improvements, it will be difficult to understand how or what improved (or hurt) the performance of your RAG system.

It is also difficult to understand what is going wrong when something does go wrong without something objective to compare against. Was it your retrieval mechanism? Was it the prompt? Is it your LLM responses? These are questions a good evaluation system can help answer.

Evaluation provides a systematic and objective way to measure the performance of your pipeline, identify areas for enhancement, and track the impact of any changes or improvements you make. Without a robust evaluation framework, it becomes challenging to understand how your RAG system is progressing and where it needs further refinement.

By embracing evaluation as an integral part of your development process, you can continuously...