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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Summary

In this chapter, we explored the critical aspect of security in RAG applications. We began by discussing how RAG can be leveraged as a security solution, enabling organizations to limit data access, ensure more reliable responses, and provide greater transparency of sources. However, we also acknowledged the challenges posed by the black box nature of LLMs and the importance of protecting user data and privacy.

We introduced the concept of red teaming a security testing methodology that involves simulating adversarial attacks to proactively identify and mitigate vulnerabilities in RAG applications. We explored common areas targeted by red teams, such as bias and stereotypes, sensitive information disclosure, service disruption, and hallucinations.

Through a hands-on code lab, we demonstrated how to implement security best practices in RAG pipelines, including techniques for securely storing API keys and defending against prompt injection attacks. We engaged in an exciting...