Applied Knowledge Graphs for LLMs

Applied Knowledge Graphs for LLMs: Designing, Implementing, and Integrating Graph-Based Context Systems

Building on the foundational concepts of intelligent and explainable AI, this second volume in the Explainable & Context-Aware AI Systems series plunges into the practical implementation of Knowledge Graphs for Large Language Models.

Tired of LLMs providing confident but incorrect answers? Ground your AI in truth.

This essential resource equips AI practitioners, data architects, and developers with the expertise to leverage Applied Knowledge Graphs for transforming Large Language Models. Learn to overcome LLM limitations like hallucinations and lack of explainability by creating powerful graph-based context systems.

Key features of this indispensable guide:

• Reinforce KG Foundations: Solidify your understanding of knowledge representation, schema design, and data modeling for Knowledge Graphs through practical application.

• LLM-Powered KG Construction: Master practical methods for automated entity and relationship extraction from unstructured text using LLMs and prompt engineering.

• Deep Integration Patterns: Implement Retrieval-Augmented Generation (RAG), multi-hop reasoning, and LLM-guided agents for superior performance.

• Operational Excellence: Discover best practices for scalable architecture, performance optimization, security, and data governance in KG-LLM environments.

• Evaluation & Improvement: Learn strategies for benchmarking KG quality and LLM performance, including human-in-the-loop processes and A/B testing.

With practical examples and a focus on real-world application, this book is your roadmap to building contextual AI solutions that are not only intelligent but also reliable, transparent, and ready for enterprise deployment.