Knowledge Graph Engineering for LLMs : Design, Build, and Integrate Graph-Based Intelligence for Context-Aware AI Systems
Language: English
Published by Amazon Digital Services LLC - Kdp Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 37.18
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Add to basketItem description from seller
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. …
Seller Inventory # 9798172644740
- Title
- Knowledge Graph Engineering for LLMs : Design, Build, and Integrate Graph-Based Intelligence for Context-Aware AI Systems
- Author
- Cyran Devereux
- Publisher
- Amazon Digital Services LLC - Kdp Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798172644740
- Item weight
- 327 grams
- Dimensions
- 254x178x10 mm
- Series
- Book 1 of 3: Mastering Knowledge Graph AI Systems
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 retrieval
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.
"Synopsis" may belong to another edition of this title.
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