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) 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

Why subscribe?

19. Other Books You May Enjoy

Customer support and chatbots with RAG

Chatbots have evolved from simple scripted responses to the complex, RAG-driven conversational agents we see today. RAG has brought the next wave of innovation to chatbots, incorporating advanced Q&A systems into the capabilities of the chatbot in a way that is significantly more conversational and natural for the user. RAG combines the best of both worlds: the ability to retrieve information from vast datasets about your company and your customers and the capability to generate coherent, contextually relevant responses. This has shown significant promise in customer support scenarios, where the ability to quickly access and leverage company-specific data, such as past customer interactions, FAQs, and support documents, has dramatically enhanced the quality of customer service.

RAG enables chatbots to provide personalized, efficient, and highly relevant responses to user queries in a way that far exceeds the performance of earlier models...