ISBN: 9798172644740 - knowledge graph engineering for llms: design, build, and integrate graph-based intelligence for context-aware ai systems: 1 (mastering knowledge graph ai systems) (6 resultados)

Taschenbuch. Condición: Neu. Neuware - Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: - Design scalable knowledge graph schemas and ontologies- Extract structured knowledge from unstructured data using modern AI techniques- Build and manage graph-based data systems for real-world applications- Query and traverse relationships to generate meaningful context- Integrate knowledge graphs with LLMs for improved accuracy and reasoning- Combine graph-based retrieval with vector search for hybrid intelligence systems- Design Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand relationships.…

Paperback. Condición: new. Paperback. Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: Design scalable knowledge graph schemas and ontologiesExtract structured knowledge from unstructured data using modern AI techniquesBuild and manage graph-based data systems for real-world applicationsQuery and traverse relationships to generate meaningful contextIntegrate knowledge graphs with LLMs for improved accuracy and reasoningCombine graph-based retrieval with vector search for hybrid intelligence systemsDesign Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand relationships. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Most AI systems today rely on unstructured data and approximate retrieval.And that is exactly where they fail.They miss context.They ignore relationships.They produce answers but not understanding.Knowledge Graph Engineering for LLMs is the starting point for building a new class of AI systems systems designed around structured knowledge, connected data, and relationship-aware intelligence.Instead of treating information as isolated text, this book teaches you how to model, connect, and organize data in ways that allow AI systems to understand how everything fits together.You will learn how to design and build knowledge graphs that integrate seamlessly with large language models giving your systems the ability to retrieve accurate context, reduce hallucinations, and generate more reliable outputs.Inside this book, you will learn how to: Design scalable knowledge graph schemas and ontologiesExtract structured knowledge from unstructured data using modern AI techniquesBuild and manage graph-based data systems for real-world applicationsQuery and traverse relationships to generate meaningful contextIntegrate knowledge graphs with LLMs for improved accuracy and reasoningCombine graph-based retrieval with vector search for hybrid intelligence systemsDesign Graph RAG pipelines that go beyond basic retrievalMore importantly, you will understand the engineering decisions behind these systems why they work, when to use them, and how to avoid the mistakes that cause most AI implementations to fail.This is not a theoretical introduction.It is a practical, system-focused guide that builds a solid foundation for advanced AI architectures preparing you for multi-hop reasoning, agentic workflows, and production-scale systems in the next volumes of the series.If you want to move beyond surface-level AI and build systems that are accurate, structured, and context-aware, this is where it begins.Go beyond text.Build systems that understand relationships. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…