Originally published on AI Tech Connect.
What you need to know The honest answer to "GraphRAG or vector RAG?" is that they fail in different places, and the skill is knowing which failure your questions will hit. Vector retrieval is the workhorse of production retrieval-augmented generation: you split a corpus into chunks, embed each one, and at query time pull back the chunks whose embeddings sit nearest to your question. It is fast, cheap, easy to build and genuinely excellent at one thing — finding the passage that already contains the answer. For "what is the refund window in our policy?" it is hard to beat, and you should reach for it first. It breaks on a specific shape of question. When the answer is not sitting inside any single chunk but is distributed across the relationships between many chunks — which of our…
Top comments (0)