Evaluation & Feedback

Thumbs up and down that ask the one follow-up question worth asking — with a state for feedback that has been sent and cannot be taken back.

Fix the answer where it is wrong, in place — the edit becomes training data, so the component keeps the original alongside it rather than overwriting it.

Two answers, side by side, with the model names hidden until a winner is picked — because a visible label is the thing being measured.

Pass rates per test case across two runs, with the regressions surfaced first and a sample size honest enough to say when a delta means nothing.

A streamed AI edit as hunks you accept or reject one by one — id-keyed decisions, word-level marks, and buttons that stay disabled while a hunk is still arriving.