Preface

We’re living through a GenAI revolution—where AI is no longer just a backend component but a copilot, content creator, and decision-maker. And yet, many GenAI applications still struggle with hallucinations, lack of contextual understanding, and opaque reasoning. That’s where this book comes in.

This book was born out of a core belief: knowledge graphs are the missing link between GenAI power and real-world intelligence. By combining the strengths of Large Language Models (LLMs) with the structured, connected data of Neo4j, and enhancing them with Retrieval-Augmented Generation (RAG) workflows, we can build systems that are not only smart but also grounded, contextual, and transparent.

We wrote this book because we’ve spent the last few years building and showcasing intelligent applications that go far beyond basic chatbot use cases. From developing AI-powered recommendation engines to integrating frameworks such as Haystack, LangChain4j, and Spring AI with Neo4j, we saw a growing need for a practical, hands-on guide that bridges GenAI concepts with production-ready knowledge graph architectures.

The vision for this book is to equip developers, architects, and AI enthusiasts with the tools, concepts, and real-world examples they need to design search and recommendation systems that are explainable, accurate, and scalable. You won’t just learn about LLMs or graphs in isolation—you’ll build end-to-end applications that bring these technologies together across cloud platforms, vector search, graph reasoning, and more.

As you journey through the chapters, you’ll go from understanding foundational concepts to implementing advanced techniques such as embedding-powered retrieval, graph reasoning, and cloud-native GenAI deployments using Google Cloud, AuraDB, and open source tools.

Whether you’re a data engineer, AI developer, or just someone curious about the future of intelligent systems, this book will help you build applications that are not only smarter but also produce better answers.