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Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG

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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Profile Icon Keith Bourne
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Paperback Sep 2024 346 pages 1st Edition
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Arrow left icon
Profile Icon Keith Bourne
Arrow right icon
€18.99 per month
Paperback Sep 2024 346 pages 1st Edition
eBook
€15.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€15.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m

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Unlocking Data with Generative AI and RAG

Part 1 – Introduction to Retrieval-Augmented Generation (RAG)

In this part, you will be introduced to retrieval-augmented generation (RAG), covering its basics, advantages, challenges, and practical applications across various industries. You will learn how to implement a complete RAG pipeline using Python, manage security risks, and build interactive applications with Gradio. We will also explore the key components of RAG systems, including indexing, retrieval, generation, and evaluation, and demonstrate how to optimize each stage for enhanced performance and user experience.

This part contains the following chapters:

  • Chapter 1, What Is Retrieval-Augmented Generation (RAG)
  • Chapter 2, Code Lab – An Entire RAG Pipeline
  • Chapter 3, Practical Applications of RAG
  • Chapter 4, Components of a RAG System
  • Chapter 5, Managing Security in RAG Applications
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Key benefits

  • Optimize data retrieval and generation using vector databases
  • Boost decision-making and automate workflows with AI agents
  • Overcome common challenges in implementing real-world RAG systems
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.

Who is this book for?

This book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.

What you will learn

  • Understand RAG principles and their significance in generative AI
  • Integrate LLMs with internal data for enhanced operations
  • Master vectorization, vector databases, and vector search techniques
  • Develop skills in prompt engineering specific to RAG and design for precise AI responses
  • Familiarize yourself with AI agents' roles in facilitating sophisticated RAG applications
  • Overcome scalability, data quality, and integration issues
  • Discover strategies for optimizing data retrieval and AI interpretability

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 27, 2024
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781835887905
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Product Details

Publication date : Sep 27, 2024
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781835887905
Category :
Concepts :

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Table of Contents

19 Chapters
Part 1 – Introduction to Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Chapter 1: What Is Retrieval-Augmented Generation (RAG) Chevron down icon Chevron up icon
Chapter 2: Code Lab – An Entire RAG Pipeline Chevron down icon Chevron up icon
Chapter 3: Practical Applications of RAG Chevron down icon Chevron up icon
Chapter 4: Components of a RAG System Chevron down icon Chevron up icon
Chapter 5: Managing Security in RAG Applications Chevron down icon Chevron up icon
Part 2 – Components of RAG Chevron down icon Chevron up icon
Chapter 6: Interfacing with RAG and Gradio Chevron down icon Chevron up icon
Chapter 7: The Key Role Vectors and Vector Stores Play in RAG Chevron down icon Chevron up icon
Chapter 8: Similarity Searching with Vectors Chevron down icon Chevron up icon
Chapter 9: Evaluating RAG Quantitatively and with Visualizations Chevron down icon Chevron up icon
Chapter 10: Key RAG Components in LangChain Chevron down icon Chevron up icon
Chapter 11: Using LangChain to Get More from RAG Chevron down icon Chevron up icon
Part 3 – Implementing Advanced RAG Chevron down icon Chevron up icon
Chapter 12: Combining RAG with the Power of AI Agents and LangGraph Chevron down icon Chevron up icon
Chapter 13: Using Prompt Engineering to Improve RAG Efforts Chevron down icon Chevron up icon
Chapter 14: Advanced RAG-Related Techniques for Improving Results Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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