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Enhance generative AI systems by integrating internal data with large language models using RAG. Instant delivery. Top rated Data products.
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
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Book 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
Foreword
Contributors
Preface
Introduction to Retrieval-Augmented Generation (RAG)
What Is Retrieval-Augmented Generation (RAG)
Code Lab – An Entire RAG Pipeline
Practical Applications of RAG
Components of a RAG System
Managing Security in RAG Applications
Components of RAG
Interfacing with RAG and Gradio
The Key Role Vectors and Vector Stores Play in RAG
Similarity Searching with Vectors
Evaluating RAG Quantitatively and with Visualizations
Key RAG Components in LangChain
Using LangChain to Get More from RAG
Implementing Advanced RAG
Combining RAG with the Power of AI Agents and LangGraph
Using Prompt Engineering to Improve RAG Efforts
Advanced RAG-Related Techniques for Improving Results
Index