Artificial Intelligence is evolving rapidly, but traditional databases often struggle to represent the complex relationships that power intelligent systems. Graph databases solve this challenge by placing relationships at the center of data modeling. In Neo4j for AI Engineers , you will learn how to design and build powerful knowledge graphs that unlock deeper insights, support smarter AI applications, and improve Retrieval-Augmented Generation (RAG) systems. Whether you are developing recommendation engines, AI-powered search systems, or intelligent applications, Neo4j provides the relationship-driven foundation required for modern AI architectures. This practical guide walks you step-by-step through the concepts, tools, and design strategies used by AI engineers to build scalable graph-powered systems. Rather than focusing only on theory, the book emphasizes real-world implementation, helping you progress from graph database fundamentals to production-ready AI infrastructure. What You Will Learn How graph databases work and why they outperform relational models for AI applications - How to design and build knowledge graphs for intelligent systems - How to use the Cypher query language to explore complex data relationships - How to build graph-powered Retrieval-Augmented Generation (RAG) pipelines - How to integrate Neo4j with AI frameworks and machine learning workflows - How to apply graph algorithms to uncover patterns and insights - How to design recommendation systems and intelligent search using graph technology - How to optimize Neo4j for performance, scalability, and production environments Who This Book Is For This book is designed for: AI Engineers - Machine Learning Engineers - Backend Developers - Data Engineers - Software Engineers building AI-powered systems If you already work with Python, APIs, machine learning frameworks, or data pipelines, this book will show you how to leverage the power of relationship-driven data using graph databases. Why Neo4j Matters for Modern AI As AI systems grow more sophisticated, understanding the relationships between data points becomes increasingly important. Graph databases enable: More accurate AI context retrieval - More effective recommendation systems - Smarter semantic search - Richer knowledge representation Organizations across industries including finance, healthcare, cybersecurity, and e-commerce use graph technology to power advanced data-driven systems. Build the Next Generation of Intelligent Systems By the end of this book, you will understand how to design, build, and scale graph-powered AI systems using Neo4j. You will gain practical knowledge at the intersection of AI engineering, data architecture, and knowledge graphs.