GraphRAG

TL;DR for operators GraphRAG usually sounds like a clean enterprise promise: put your knowledge into a graph, attach it to a language model, and enjoy more grounded answers. The less glamorous truth is that someone has to build the graph. At web scale, that “someone” is usually an LLM being asked to extract triples from millions or billions of passages, which is a fine idea if the procurement team has recently discovered oil under the server room. ...

TL;DR for operators Enterprise RAG teams often treat retrieval quality as a graph-construction problem: extract more entities, more relationships, more summaries, and hope the answer appears somewhere in the resulting machinery. Clue-RAG suggests a more useful diagnosis: the failure is often not that the graph is too small, but that the system has chosen the wrong semantic unit for the job.1 ...