Unlocking Data with Generative AI and RAG

Keith Bourne’s second edition of Unlocking Data with Generative AI and RAG arrives at a moment when retrieval-augmented generation has moved from experimental curiosity to the backbone of reliable AI applications. This book does more than introduce concepts—it walks you through building a working RAG pipeline, adding agent-like memory and graph-based retrieval, and thinking about security in production systems.

From First Principles to Working Code 💻

The early chapters establish a clear technical foundation: vectors, embeddings, context windows, prompting, and the role of frameworks like LangChain and LlamaIndex. Then the book shifts into hands-on territory with a code lab that constructs an entire RAG pipeline step by step—from installing dependencies and loading documents to embedding chunks, retrieving context, and generating responses.

Beyond Basic RAG 🧠

The second edition expands into areas that matter for real deployments. Graph-based RAG, memory systems for agents, and intelligent recall are framed not as abstract trends but as practical techniques that influence how systems store, retrieve, and reason over data. The table of contents also includes dedicated coverage of practical applications, system components, and security.

What You Can Expect to Build

• A working retrieval-augmented generation pipeline using OpenAI and LangChain
• RAG applications for customer support, reporting, product recommendations, and knowledge-base search
• A mental model of retrieval, generation, pre- and post-processing, and output interfaces
• Security-minded approaches that limit data exposure and control what the model can access

Who This Book Is For 🎯

Developers, data engineers, and technical founders who already work with Python and want to move beyond API calls will find the code labs immediately useful. The material also suits engineers evaluating whether RAG or fine-tuning is the right approach, and team leads who need to understand trade-offs before committing to an architecture.

Updated, Expanded, and Production-Aware

First published in late 2024, this second edition reflects the rapid shifts in AI tooling and agent design. The writing stays grounded in the author’s production experience, while keeping the explanations clear enough for readers who are new to vector stores and retrieval pipelines. Each chapter balances conceptual depth with practical examples.

Digital Delights offers this ebook as an instant download, ready to load onto your preferred reading device or development machine. If you want a hands-on route into retrieval-augmented generation that covers both the fundamentals and the emerging agentic techniques, this is a strong place to start.