Chunk-and-embed is not RAG. Here is the 7-stage pipeline that actually grounds answers, with the eval harness that catches hallucinations before deploy.
Most RAG implementations are chunk-and-embed plus a top-k search. That is a starting point, not an architecture. Below: the seven-stage pipeline that actually grounds answers, with the eval harness that catches hallucinations before they ship. The tradeoffs we made and why Every architecture choice in this category is a tradeoff. Here are the ones we have made consciously, and the alternative we did not pick.
We use Pipecat instead of building our own orchestration. The win is months of saved engineering. The cost is being a release behind on a few model integrations. We use Anthropic as the default LLM with OpenAI failover, not the other way around. The win is consistently lower hallucination rates in our eval set.
Chunk-and-embed is not RAG. Here is the 7-stage pipeline that actually grounds answers, with the eval harness that catches hallucinations before deploy. It is filed under AI Infrastructure because that is where operators looking for this problem actually start, and it is written from production work rather than from a content calendar.