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Artículo: AMZ-B0G2M28FM8
Practical Knowledge Graphs for Next Gen LLMs: Architecture, Context Integration, RAG Optimization, and Explainability Techniques for Enterprise AI
Format:
Kindle
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0.84 kg
0.84 kg
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Producto de
Amazon
Amazon
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USA
USA
Sobre este producto
- Practical Knowledge Graphs for Next-Gen LLMs: Architecture, Context Integration, RAG Optimization, and Explainability Techniques for Enterprise AI is a comprehensive, engineering-focused guide designed for practitioners building reliable, scalable, and interpretable AI systems. This book bridges the gap between modern Large Language Models (LLMs) and structured knowledge technologies, providing a hands-on framework for architecting enterprise-grade solutions that combine symbolic reasoning with neural intelligence. Targeted at data engineers, AI architects, ML researchers, and enterprise developers, the book explores how knowledge graphs (KGs) can be used to enhance LLM performance across key dimensions contextual accuracy, retrieval precision, reduced hallucination, traceability, and operational robustness. Readers will learn how to design and implement KG-driven pipelines that support next-generation Retrieval-Augmented Generation (RAG), semantic search, entity linking, dynamic context construction, and multi-step reasoning. Through detailed system architectures, reproducible workflows, and practical code examples, the book covers end-to-end KG integration: from ontology engineering, graph modeling, and embedding strategies to vector-symbolic fusion, hybrid retrieval stacks, orchestration patterns, and observability tools for production AI. Special attention is given to explainability techniques that use graph-based evidence trails, provenance tracking, and structured grounding to make AI outputs verifiable and trustworthy. This book equips readers with the methodologies and design patterns required to build advanced AI systems that are context-aware, interpretable, scalable, and optimized for real-world enterprise deployment.
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