Regression Datasets
Every bug we fix becomes a permanent test case — so the same failure can never sneak back in with a future change.
Aviation got safe because every incident became a checklist item. Regression datasets bring that habit to AI workflows: each fixed failure is written down as a test that every future change must pass. 'We'll never make that mistake twice' stops being a hope and becomes a rule the machinery enforces.
The problem, in plain words
A customer hits a bug. Your team scrambles, finds the cause, fixes it, apologizes. Everyone moves on. Six months later, a completely unrelated change quietly reintroduces the same failure — and the same customer hits it again. Nothing was in place to notice, because the fix lived in the code but the lesson lived nowhere. Teams that don't write their failures down are, in a very literal sense, planning to repeat them.
What we set up
Every failure the team triages becomes a candidate regression case: the exact input that triggered it, the bad output it produced, and the corrected output it should have produced. A curation step removes duplicates and labels each case, recording where it came from and when (its provenance, versioned like everything else). The result joins the eval set (the saved collection of test cases) that the harness runs on every single change — so a change that would bring an old bug back fails the tests before it ships, not after.
How it works, step by step
- A failure happens and gets fixed
Normal life with any system. The difference is what happens next.
- The failure is written down as a case
The triggering input, the wrong output, the right output — the whole incident, captured as a test.
- Curation cleans the pile
Duplicates merged, each case labeled, its origin and date on record. A tidy set, not a junk drawer.
- The case joins the permanent set
From now on, it is part of what every future change is tested against, automatically.
- Old bugs can't sneak back
A change that reintroduces a known failure fails the tests and never ships. The lesson holds.
What changes for you
Before, fixing a bug protected you until the next big change. After, the test set grows with every incident, so the system gets steadily harder to break in the ways it has already broken — quality drift becomes detectable, and repeat failures become rare. The eval set compounds in value as the system matures, like a savings account of hard-won lessons. What it won't do: it won't catch brand-new kinds of failure. It guards against the mistakes you've already paid for once — the new ones still need watching.