Engineering Knowledge Graphs for LLM Applications: Schema Design, Ontologies, RAG Pipelines, Graph Databases, and Context-Aware AI Systems - Brossura
Sinossi
Artificial Intelligence is evolving rapidly, but even the most advanced Large Language Models (LLMs) still struggle with one critical limitation: lack of structured, reliable context. Hallucinations, inconsistent reasoning, and limited explainability continue to hold back real-world deployment.
Engineering Knowledge Graphs for LLM Applications addresses this challenge head-on.
This comprehensive, implementation-focused guide shows you how to design and integrate knowledge graphs into modern AI systems, transforming LLMs into context-aware, reliable, and explainable solutions suitable for production environments.
Rather than focusing on theory alone, this book delivers a practical, end-to-end approach to building knowledge-driven AI systems. You’ll learn how to create structured data layers that serve as a single source of truth, enabling LLMs to reason more accurately and generate grounded outputs.
What You’ll Learn
- How to design scalable knowledge graph architectures for LLM systems
- Principles of schema design, ontology modeling, and semantic data structures
- Techniques for entity extraction, relationship discovery, and automated graph population
- How to build and integrate Retrieval-Augmented Generation (RAG) pipelines
- Methods for multi-hop reasoning and context-aware AI workflows
- How to connect graph databases to LLM applications for real-time intelligence
- Strategies for reducing hallucinations and improving response accuracy
- Approaches to semantic search, context fusion, and knowledge-guided agents
- Best practices for scalability, performance optimization, and system design
- Governance, versioning, and production-grade deployment of knowledge-driven AI systems
This book is designed for:
- Machine Learning Engineers building LLM-powered systems
- Data Engineers and Architects working with structured data and pipelines
- AI Researchers exploring hybrid AI + knowledge systems
- Backend and Platform Engineers integrating AI into real-world applications
- Enterprise teams seeking reliable, explainable AI solutions
As AI systems move from experimentation to production, structured knowledge is becoming essential. Pure LLM-based approaches are no longer enough for applications that demand accuracy, transparency, and trust.
By combining knowledge graphs, semantic modeling, and LLM architectures, this book equips you to build AI systems that:
- Deliver more accurate and context-aware outputs
- Provide traceable and explainable reasoning
- Scale across complex, real-world data environments
- Support mission-critical and enterprise-grade applications
If you want to go beyond basic prompt engineering and build robust, knowledge-driven AI systems, this book gives you the tools, patterns, and engineering strategies to do it right.
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Engineering Knowledge Graphs for LLM Applications
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Engineering Knowledge Graphs for LLM Applications (Paperback)
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Paperback. Condizione: new. Paperback. Artificial Intelligence is evolving rapidly, but even the most advanced Large Language Models (LLMs) still struggle with one critical limitation: lack of structured, reliable context. Hallucinations, inconsistent reasoning, and limited explainability continue to hold back real-world deployment.Engineering Knowledge Graphs for LLM Applications addresses this challenge head-on.This comprehensive, implementation-focused guide shows you how to design and integrate knowledge graphs into modern AI systems, transforming LLMs into context-aware, reliable, and explainable solutions suitable for production environments.Rather than focusing on theory alone, this book delivers a practical, end-to-end approach to building knowledge-driven AI systems. You'll learn how to create structured data layers that serve as a single source of truth, enabling LLMs to reason more accurately and generate grounded outputs.What You'll LearnHow to design scalable knowledge graph architectures for LLM systemsPrinciples of schema design, ontology modeling, and semantic data structuresTechniques for entity extraction, relationship discovery, and automated graph populationHow to build and integrate Retrieval-Augmented Generation (RAG) pipelinesMethods for multi-hop reasoning and context-aware AI workflowsHow to connect graph databases to LLM applications for real-time intelligenceStrategies for reducing hallucinations and improving response accuracyApproaches to semantic search, context fusion, and knowledge-guided agentsBest practices for scalability, performance optimization, and system designGovernance, versioning, and production-grade deployment of knowledge-driven AI systemsWho This Book Is ForThis book is designed for: Machine Learning Engineers building LLM-powered systemsData Engineers and Architects working with structured data and pipelinesAI Researchers exploring hybrid AI + knowledge systemsBackend and Platform Engineers integrating AI into real-world applicationsEnterprise teams seeking reliable, explainable AI solutionsWhy This Book MattersAs AI systems move from experimentation to production, structured knowledge is becoming essential. Pure LLM-based approaches are no longer enough for applications that demand accuracy, transparency, and trust.By combining knowledge graphs, semantic modeling, and LLM architectures, this book equips you to build AI systems that: Deliver more accurate and context-aware outputsProvide traceable and explainable reasoningScale across complex, real-world data environmentsSupport mission-critical and enterprise-grade applicationsBuild the Next Generation of AI SystemsIf you want to go beyond basic prompt engineering and build robust, knowledge-driven AI systems, this book gives you the tools, patterns, and engineering strategies to do it right. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798258413482
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