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Engineering Knowledge Graphs for LLM Applications: Schema Design, Ontologies, RAG Pipelines, Graph Databases, and Context-Aware AI Systems
Paperback
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ISBN13: 9798258413482
Publisher: Independently Published
Published: Apr 22 2026
Pages: 212
Weight: 0.83
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Apr 22 2026
Pages: 212
Weight: 0.83
Height: 0.45 Width: 7.00 Depth: 10.00
Language: English
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
This book is designed for:
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:
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.
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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