Advanced Retrieval-Augmented Generation

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. 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.

Pages: 560, Edition: 1, Hardcover, Wiley-IEEE Press