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Knowledge Graph Engineering for LLM Systems: Building Structured Context, Reducing Hallucinations, and Integrating Graph-Based Intelligence into Large
Paperback
Publisher: Independently Published
Published: Nov 9 2025
Pages: 284
Weight: 1.09
Height: 0.60 Width: 7.00 Depth: 10.00
Language: English
Readers learn modern techniques for automated entity extraction, relationship discovery, schema population, and graph enrichment using advanced LLM prompting and hybrid NLP methods. The book outlines multiple integration patterns including Retrieval-Augmented Generation (RAG), multi-hop reasoning workflows, context fusion layers, and knowledge-guided agent architectures showing how to connect graph intelligence to model outputs.
A full coverage of operational considerations is included, such as scalable graph storage, query optimization, system performance, security models, version control, and governance frameworks required for production-grade KG-LLM deployments. Detailed evaluation strategies for measuring graph quality, LLM accuracy, contextual relevance, and end-to-end pipeline performance are also provided.
By bridging semantic technologies and modern AI systems, this book equips professionals to build context-aware, transparent, and highly dependable AI solutions capable of addressing hallucinations, improving explainability, and supporting critical enterprise applications.
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