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 limitations RAG pipeline engineering chunking, indexing, retrieval, ranking, and generation KG construction and analytics schema design, extraction techniques, graph algorithms, embeddings, and GNNs Graph-RAG architectures and evaluation graph-based retrieval, graph-assisted generation, hybrid LLM KG workflows, frameworks, benchmarks, and metrics Emerging directions multimodal KGs, dynamic graphs, explainable RAG, RL-based traversal, and enterprise-scale implementations With extensive hands-on examples and production-ready patterns, Advanced Retrieval-Augmented Generation is an indispensable resource for AI practitioners, ML engineers, researchers, and architects building the next generation of reliable, knowledge-grounded AI systems.
