As powerful as large language models (LLMs) have become, they often fail to deliver accurate, explainable results due to their limited access to reliable data. GraphRAG is the next evolution of the foundational retrieval-augmented generation (RAG) architecture, combining the structured intelligence of knowledge graphs with the power of LLMs to deliver deeper, more trustworthy outputs. This book gives you the tools, skills, and confidence to make sense of this complex approach.

Go beyond simple vector search to discover how graph-powered retrieval offers context-rich, explainable answers at scale. Written by industry leaders in knowledge graphs and generative AI, this concise and practical guide gives developers, AI engineers, and data scientists a complete introduction to GraphRAG, from core concepts to production-ready applications. Through real-world examples and proven best practices, you'll gain the skills to integrate GraphRAG into your AI stack and build smarter, more reliable systems.

• Learn how to model, build, and apply knowledge graphs to structured and unstructured data
• Implement GraphRAG techniques to improve the accuracy and explainability of GenAI applications
• Apply a broad set of retrieval patterns for agentic systems and advanced AI workflows
• Understand business use cases and research innovations driving this technology forward
• Turn GraphRAG concepts into scalable GenAI applications