ISBN: 9781394374687 - advanced retrieval-augmented generation: bridging large language models and knowledge graphs (15 resultados)

Condición: New.

Hardcover. Condición: new. Hardcover. Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation Large language models are powerfulbut 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, ranking, and generationKG construction and analytics schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation graph-based retrieval, graph-assisted generation, hybrid LLMKG workflows, frameworks, benchmarks, and metricsEmerging 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New.

Hardback. Condición: New. 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, ranking, and generationKG construction and analytics - schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation - graph-based retrieval, graph-assisted generation, hybrid LLM-KG workflows, frameworks, benchmarks, and metricsEmerging 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.…

Condición: New.

Hardcover. Condición: Brand New. 560 pages. 7.25x1.48x10.18 inches. In Stock.

Hardcover. Condición: Brand New. 560 pages. 7.25x1.48x10.18 inches. In Stock.

Hardcover. Condición: new. Hardcover. Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation Large language models are powerfulbut 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, ranking, and generationKG construction and analytics schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation graph-based retrieval, graph-assisted generation, hybrid LLMKG workflows, frameworks, benchmarks, and metricsEmerging 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. Neuware - 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.…

Hardback. Condición: New. 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, ranking, and generationKG construction and analytics - schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation - graph-based retrieval, graph-assisted generation, hybrid LLM-KG workflows, frameworks, benchmarks, and metricsEmerging 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.…

Condición: New. 2026. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condición: new. Hardcover. Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation Large language models are powerfulbut 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, ranking, and generationKG construction and analytics schema design, extraction techniques, graph algorithms, embeddings, and GNNsGraph-RAG architectures and evaluation graph-based retrieval, graph-assisted generation, hybrid LLMKG workflows, frameworks, benchmarks, and metricsEmerging 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. 2026. 1st Edition. hardcover. . . . . .