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)

2. Chapter 1: What Is Retrieval-Augmented Generation (RAG) FREE CHAPTER

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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19. Other Books You May Enjoy

Evaluate as you build

Evaluation plays a crucial role throughout the development process of a RAG pipeline. By continuously evaluating your system as you build it, you can identify areas that need improvement, optimize the system’s performance, and systematically measure the impact of any modifications or enhancements you make.

Evaluation is essential for understanding the trade-offs and limitations of different approaches within the RAG pipeline. RAG pipelines often involve various technical choices, such as the vector store, the retrieval algorithm, and the language generation model. Each of these components can have a significant impact on the overall performance of the system. By systematically evaluating different combinations of these components, you can gain valuable insights into which approaches yield the best results for your specific tasks and domain.

For instance, you might experiment with different embedding models, such as local open source models that you...

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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

Evaluate as you build

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Table of Contents (20) Chapters

Evaluate as you build

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