Advanced Retrieval-Augmented Generation: Bridging Large Language Models and Knowledge Graphs

Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation Large language models are powerful-but they hallucinate.Advanced Retrieval-Augmented Generation offers a complete guide from the foundations of information retrieval (IR) to the cutting-edge frontiers of RAG.Bridging large language models (LLMs) and knowledge graphs (KGs), this book provides the theoretical principles, practical techniques, and hands-on frameworks needed to build reliable AI systems that minimize hallucinations and improve factual correctness.The book covers core concepts of Graph-RAG with applications across search, recommendation, and enterprise AI.Practical chapters demonstrate implementations using LlamaIndex, Neo4j, and leading Graph-RAG frameworks.Readers will learn: IR and LLM fundamentals - model paradigms, transformer architecture, model families, training techniques, prompt engineering, applications, and limitationsRAG pipeline engineering -chunking, indexing, retrieval,