Large language models (LLMs) are impressive — until they are not. If you ask one about your internal data, your product catalog, or your users' reviews, it will either hallucinate an answer or admit it does not know. The solution most teams reach for is retrieval-augmented generation (RAG). This retrieves relevant data first, injects it into the prompt as context, and lets the model answer from that context rather than from memory. GraphRAG takes this a step further. Instead of retrieving only text chunks, it can use graph relationships to retrieve connected context, following relationships between entities to build richer, more structured context. The result can provide answers grounded in both data and the relationships between that data.