AI app changes can fail quietly. A prompt tweak, model swap, retrieval change, or schema update can still return plausible answers while breaking required fields, citations, length limits, or safety wording.
Here is the smallest clean-room routine I use before release:
- Use synthetic fixtures only. Do not use customer logs, secrets, support tickets, private documents, or production prompts.
- Define expected output checks in plain rules: required fields, forbidden claims, required citations, length bounds, and valid JSON when needed.
- Run the same scenarios before every prompt/model/RAG change.
- Generate one pass/fail release note so the team can see exactly what changed.
- Keep one human review step for edge cases deterministic checks cannot judge.
The first three checks to start with
- required fields exist
- forbidden wording is absent
- output length stays inside expected bounds
If useful, I packaged a tiny starter kit with synthetic examples, templates, and a local runner:
https://cleanfixture-kit.kevinskysunny.workers.dev
It is intentionally clean-room: no internal company data, no customer examples, and no claim that it replaces compliance or safety review.