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

Wei, Wendy Ran; Wu, Huijun

Hardcover

ISBN 10: 1394374682 ISBN 13: 9781394374687

Publisher: Wiley-IEEE Press, 2026

Synopsis

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.

About this title

About the Author

Wendy Ran Wei, PhD, is an expert in AI, ML, and LLMs, specializing in search and recommendation systems. She is a Machine Learning Engineer at Airbnb, where she develops retrieval and ranking models and brings LLM technologies into production. She previously held engineering roles at Meta, Pinterest, and Twitter, building large-scale search and recommendation solutions. Dr. Wei received her PhD in Statistics from The Ohio State University.

Huijun Wu, PhD, is an Engineer at Samsung Research America with expertise in large-scale distributed systems and data processing. He received his PhD in Computer Science from Arizona State University.

From the Back Cover

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.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2026
• Language: English
• ISBN 10: 1394374682
• ISBN 13: 9781394374687
• Binding: Hardcover
• Edition number: 1
• Number of pages: 560