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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.

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