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

The evaluation component is essential for assessing and improving the RAG system’s performance. While there are many common practices for evaluation, the most effective evaluation system will focus on what is most important for your users and provide an evaluation for improving those features and capabilities. Often, this involves analyzing the system’s outputs using various metrics, such as accuracy, relevance, response time, and user satisfaction. This feedback is used to identify areas of improvement, and guide adjustments in the system’s design, data handling, and LLM integration. Continuous evaluation is crucial for maintaining high-quality responses and ensuring that the system meets users’ needs effectively.

As mentioned previously, you can also collect user feedback in various ways, including qualitative data (entry forms with open-ended questions) or quantitative (true/false, ratings, or other numerical representations) on the usefulness...