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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Code lab 5.2 – Red team attack!

This code can be found in the CHAPTER5-2_SECURING_YOUR_KEYS.ipynb file in the CHAPTER_05 directory of the GitHub repository.

Through our hands-on code lab, we will engage in an exciting red team versus blue team exercise, showcasing how LLMs can be both a vulnerability and a defense mechanism in the battle for RAG application security.

We will first take the role of red team and orchestrate a prompt probe on our RAG pipeline code. As mentioned earlier in this chapter, prompt probing is the initial step to gain insight into the internal prompts a RAG system is using to discover the system prompt(s) of a RAG application. The system prompt is the initial set of instructions or context provided to the LLM to guide its behavior and responses. By uncovering the system prompt, attackers can gain valuable insights into the inner workings of the application and this sets the foundation for designing more targeted and efficient attacks using the other...