RAG for developers who aren't AI engineers: what actually matters
Most non-AI developers have a mental model of RAG that is either wrong or dangerously incomplete. Not because they're bad engineers — because almost everything written about RAG is either a 10-minute framework tutorial that hides every real decision, or a research paper aimed at people who already do this for a living. There's very little in between.
This article is the in-between. I recently built a production RAG system over 62 ancient-history books (~46,000 chunks) and measured every design decision against a fixed test set — including the decisions that didn't work. I'm going to skip the framework marketing and tell you the small number of things that actually determine whether your RAG system is trustworthy, with the numbers to back them up.
You don't need ML knowledge to follow this. If you can build a REST API and query Postgres, you can build everything described here.
The 60-second mental model
