What is GraphRAG? How Knowledge Graphs Make LLMs More Accurate

GraphRAG is an advanced retrieval-augmented generation pattern that enhances the traditional RAG approach by incorporating a knowledge graph to provide structured, connected context for large language models (LLMs). Unlike vector-only RAG, which retrieves text based on semantic similarity, GraphRAG retrieves entities, relationships, and grounded text to enable LLMs to answer multi-step, policy-aware, and relational questions with greater accuracy and explainability. This approach is particularly beneficial for enterprise AI applications in domains such as financial services, healthcare, supply chain, compliance, and customer intelligence, where understanding the connections between business entities is crucial. By shifting from a focus on finding relevant content to assembling connected evidence, GraphRAG addresses the limitations of vector-only systems that struggle with complex reasoning and relational queries, thereby improving the quality and trustworthiness of AI-generated responses. TigerGraph's platform supports this architecture by combining native graph traversal and hybrid graph-and-vector retrieval, making it suitable for production-scale AI systems that require reliable and explainable outputs.

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