A RAG app can look impressive in a demo and still fail the first week real users touch it.
The dangerous part is not always an obvious hallucination. It is the quiet failure: the answer sounds right, the citation looks official, the user moves on, and your SaaS just taught someone the wrong workflow.
If you are building an AI SaaS product with retrieval-augmented generation, you do not need a giant evaluation lab on day one. You need a small, repeatable RAG evaluation checklist that catches bad retrieval, weak grounding, citation mismatch, and regressions before they reach production.
This guide is for solo SaaS developers, AI SaaS builders, and small technical teams that need practical evaluation without turning the product into a research project.
Why RAG evaluation matters more than another prompt tweak
