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Knowledge Graphs for LLMs

Knowledge graphs transform how LLMs retrieve, ground, and explain their outputs. Rather than searching flat vector embeddings alone, a knowledge graph explicitly models entities—people, concepts, organizations—and their semantic relationships, enabling LLMs to perform multi-hop reasoning, cite authoritative sources, and deliver provably correct answers.

This 10-article series guides you from graph fundamentals through production-scale deployment. You'll learn to extract entities and relationships from raw text, build queryable graph structures, integrate graphs with RAG pipelines, and create explainable LLM outputs anchored to knowledge sources. Each article includes working code, real-world examples, and best practices refined from deployed systems.

By the end, you'll understand when and how to add structured knowledge to your LLM stack—and why Google, OpenAI, and enterprise AI teams are shifting toward graph-augmented retrieval.

Articles in this series​