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

View More author details

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

Why subscribe?

19. Other Books You May Enjoy

Additional evaluation techniques

Ragas is just one of many evaluation tools and techniques available to evaluate your RAG system. This is not an exhaustive list, but in the following subsections, we will discuss some of the more popular techniques you can use to evaluate the performance of your RAG system, once you have obtained or generated ground-truth data.

Bilingual Evaluation Understudy (BLEU)

BLEU measures the overlap of n-grams between the generated response and the ground-truth response. It provides a score indicating the similarity between the two. In the context of RAG, BLEU can be used to evaluate the quality of the generated answers by comparing them to the ground-truth answers. By calculating the n-gram overlap, BLEU assesses how closely the generated answers match the reference answers in terms of word choice and phrasing. However, it’s important to note that BLEU is more focused on surface-level similarity and may not capture the semantic meaning or relevance...

The rest of the chapter is locked

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

Additional evaluation techniques

Bilingual Evaluation Understudy (BLEU)

Authors (1)

Personalised recommendations for you

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

Additional evaluation techniques

Bilingual Evaluation Understudy (BLEU)

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you