Knowledge Graphs for LLMs: A Hands-On Guide to Building Trustworthy AI

Knowledge Graphs for LLMs: A Hands-On Guide to Building Trustworthy AI
Build reliable, transparent, and context-aware AI by integrating knowledge graphs with large language models.
Are you frustrated by LLMs confidently generating incorrect or misleading answers? It's time to ground your AI in structured truth.
This practical guide empowers AI practitioners, data scientists, and developers to combine the interpretability of Knowledge Graphs (KGs) with the generative power of Large Language Models (LLMs) to create trustworthy, explainable, and high-performing AI systems. Designed for real-world application, Knowledge Graphs for LLMs demystifies the process of integrating symbolic knowledge with neural models-unlocking the next evolution of intelligent systems.
Key features of this essential guide:

• Foundations First
  Reinforce your understanding of KG design, data modeling, and knowledge representation through accessible, example-driven explanations.
• Build with LLMs
  Learn to automate KG construction using modern NLP techniques, entity extraction, and prompt-engineered LLM pipelines.
• Integration that Matters
  Implement cutting-edge approaches like Retrieval-Augmented Generation (RAG), LLM-augmented reasoning, and hybrid KG-LLM architectures.
• Operate at Scale
  Discover architectural best practices, scalability strategies, and security considerations for enterprise-ready deployment.
• Evaluate and Evolve
  Benchmark your KG-LLM systems with real-world metrics, human-in-the-loop feedback, and structured A/B testing.

Filled with hands-on tutorials, code examples, and real-world case studies from domains like healthcare and materials science, this book equips you to design contextual AI that's not only powerful-but also accurate, transparent, and responsibly built for the future.