Retrieval-augmented generation (RAG) helps AI find information, but it doesn't help AI understand how pieces of information relate. Knowledge graphs fill this gap by structuring data as entities and relationships, giving AI a semantic map of your business. This article walks through the fundamentals of knowledge graphs, from ontology design to graph storage, and then explores practical integration with large language models. Real-world case studies across industries show how enterprises use this combination to power recommendation engines, fraud detection, and intelligent search. For teams already using RAG, adding a knowledge graph layer can dramatically improve answer accuracy and reasoning capability. The guide also addresses common pitfalls like data modeling complexity and query performance, offering pragmatic advice for starting small and scaling.
Knowledge graphs go beyond RAG by modeling relationships, enabling AI to truly understand enterprise data. This guide covers concepts, implementation, and LLM integration.