Unlocking Data with Generative AI and RAG: Enhance Generative AI systems by integrating internal data with Large Language Models using RAG

Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantage

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Key Features

• 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

Book Description

Generative AI is helping organizations tap into their data in new ways, with RAG combining the strengths of 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.

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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

Who this book is 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.

Table of Contents

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

Keith Bourne is a senior Generative AI data scientist at Johnson & Johnson. He has over a decade of experience in machine learning and AI working across diverse projects in companies that range in size from start-ups to Fortune 500 companies. With an MBA from Babson College and a master's in applied data science from the University of Michigan, he has developed several sophisticated modular Generative AI platforms from the ground up, using numerous advanced techniques, including RAG, AI agents, and foundational model fine-tuning. Keith seeks to share his knowledge with a broader audience, aiming to demystify the complexities of RAG for organizations looking to leverage this promising technology.