Why do "vibe checks" fail, and what does eval-driven development look like?

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Vibe checks (trying a few prompts by hand and judging by feel) are biased toward memorable examples, can't detect small regressions, don't cover edge cases, and aren't reproducible. Eval-driven development means defining success criteria and a dataset first, running evals on every change, analysing errors to decide the next improvement, and tracking metrics over time, much like test-driven development but statistical.

The eval-driven development loop: define criteria, build a dataset, baseline, analyse errors, change one thing and re-run, looping back when worse, shipping when better, and feeding production failures back into the dataset.

Why vibe checks mislead

Selection bias: you test what you remember or expect to work.
Small samples: improving 3 visible examples can break 20 others.
Anchoring: you judge more leniently after a long day of prompt tweaking.
No history: you can't compare against last week's version.

Vibe checks still have a role: quick exploration early on, and reading outputs is essential for error analysis. But decisions should come from measurements.

Interview signal. Describing a concrete loop with metrics, a dataset and error analysis, and mentioning one change at a time.

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Why do "vibe checks" fail, and what does eval-driven development look like?

Why vibe checks mislead

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