Beyond Vector Search: Why GraphRAG is the Next Frontier for LLMs For the past year, the industry standard for augmenting LLMs has been Retrieval-Augmented Generation (RAG) using vector databases. We chunk documents, embed them into vectors, and perform similarity searches. But as projects grow in complexity, we hit a wall: vector search is great at finding snippets , but terrible at understanding contextual relationships . The Problem: The "Isolated Snippet" Trap Traditional RAG treats information as isolated fragments. If you ask an LLM, "How do the changes in our 2023 infrastructure impact our cloud spending?", vector search might pull relevant paragraphs about infrastructure and paragraphs about spending, but it lacks the explicit link between those two entities. Enter GraphRAG GraphRAG (Graph Retrieval-Augmented Generation) bridges this gap by representing data as a Knowledge Graph.…