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)

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

Why subscribe?

19. Other Books You May Enjoy

Implementing RAG in AI applications

RAG is rapidly becoming a cornerstone of generative AI platforms in the corporate world. RAG combines the power of retrieving internal or new data with generative language models to enhance the quality and relevance of the generated text. This technique can be particularly useful for companies across various industries to improve their products, services, and operational efficiencies. The following are some examples of how RAG can be used:

Customer support and chatbots: These can exist without RAG, but when integrated with RAG, it can connect those chatbots with past customer interactions, FAQs, support documents, and anything else that was specific to that customer.
Technical support: With better access to customer history and information, RAG-enhanced chatbots can provide a significant improvement to current technical support chatbots.
Automated reporting: RAG can assist in creating initial drafts or summarizing existing articles...

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

Implementing RAG in AI applications

Authors (1)

Personalised recommendations for you