Unbundling the Graph in GraphRAG

One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. The haphazard results may be entertaining, although not quite based in fact. RAG provides a way […]