What if the future of AI systems could actually think in context - not just predict words, but reason through knowledge?
Building Knowledge Graphs for LLMs reveals how to merge the symbolic power of knowledge graphs with the generative strength of large language models (LLMs) to create smarter, context-aware AI systems. It's a hands-on guide for engineers, data scientists, and researchers who want to design systems that reason with precision, accuracy, and structure.
This book walks readers through every stage of development, from conceptual design to practical deployment, showing how graph-enhanced context systems solve LLM limitations like hallucination, factual grounding, and reasoning over relationships. Through clear explanations and working examples, you'll learn how to build intelligent architectures that connect data, meaning, and inference seamlessly.
Readers will explore key chapters such as:
Introduction to Graph-Enhanced Context Systems - Understand how knowledge graphs complement and stabilize LLMs.
Design Principles and Ontology Modeling - Learn how to model entities, relationships, and semantics for real-world applications.
Building and Populating the Graph - Use structured and unstructured data pipelines to create scalable, reliable graphs.
Storage and Retrieval Systems - Discover how to query, index, and optimize graph data for high-performance LLM integration.
Integrating with LLM Pipelines - Implement RAG and GraphRAG patterns using tools like Neo4j, LangChain, and LlamaIndex.
Evaluation, Scaling, and Case Studies - Test, refine, and deploy production-ready graph-augmented AI systems across enterprise, research, and legal domains.
Unlike generic AI handbooks, this work bridges symbolic AI and neural architectures, offering practical frameworks, code patterns, and case studies drawn from real-world deployments. Readers won't just understand knowledge graphs; they'll know how to use them to transform how LLMs reason, retrieve, and respond.
Whether you're building advanced chat systems, enterprise knowledge engines, or AI reasoning frameworks, this book equips you with the architecture, tools, and techniques to build graph-enhanced intelligence that truly understands context.
Get your copy today and build AI systems that think beyond text, systems that understand knowledge.
