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Unlocking Data with Generative AI and RAG

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

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887905
Length 346 pages
Edition 1st Edition
Concepts
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Author (1):
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Keith Bourne Keith Bourne
Author Profile Icon Keith Bourne
Keith Bourne
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Table of Contents (20) Chapters Close

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 19. Other Books You May Enjoy

Practical Applications of RAG

In Chapter 1, we listed several ways retrieval-augmented generation (RAG) is being implemented in AI applications, such as customer support with chatbots, automated reporting, product descriptions, searchability and utility of knowledge bases, innovation scouting, content personalization, product recommendations, and training and education.

In this chapter, we will cover the following topics:

  • Customer support and chatbots with RAG
  • RAG for automated reporting
  • E-commerce support
  • Utilizing knowledge bases with RAG
  • Innovation scouting and trend analysis
  • Content personalization for media and content platforms
  • Leveraging RAG for personalized recommendations in marketing communications
  • Training and education
  • Code lab 3.1 – Adding sources to your RAG

These topics should provide a comprehensive understanding of the broad scope and versatility of RAG.

The examples presented in this chapter are not meant...

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