Guides

11 min read

Designing golden datasets that actually catch regressions

A practical guide to building eval sets that reflect real traffic instead of the demo you gave last quarter.

Priya Raman

A golden dataset is a promise: if these cases pass, users will be happy. Most datasets break that promise because they were written once, by one person, on a Friday afternoon.

The problem with doing it by hand

Manual review does not scale past a handful of prompts. Changes pile up, context gets lost in chat threads and nobody can say which version is actually running in production. Worse, the feedback loop is measured in days, not seconds.

What we do instead

We treat prompts as code. Each change is a versioned commit with a diff, an owner and an automatic evaluation run. Nothing merges unless it holds up against real traffic.

Three rules we never break

  • Every prompt change runs against a golden dataset before merge.

  • Every production call is traced, with cost and latency attached.

  • Every deploy can be rolled back in one click, without a code release.

The best prompt is the one you can prove is better than the last one.

If you want to try this workflow on your own prompts, the free plan includes everything in this post. Start at zero, ship today.

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