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Evals aren't tests, and that's okay

What evals borrow from software tests, what they can't, and how to keep your team from treating them as interchangeable.

Initial Editor·2026-03-15·3min read·239 words

Every team new to LLMs tries to treat evals like unit tests. Pass or fail. Green or red. Block the deploy if anything goes red.

It doesn't work.

Why evals are different

Tests assert exact behavior. Evals measure distributional behavior. The difference matters more than it sounds.

  • A unit test either passes or fails. An eval produces a score.
  • A unit test is stable across runs. An eval is noisy — same inputs, different outputs.
  • A unit test says "this specific thing is broken." An eval says "the model got 67% right, up from 65%."

If you treat an eval as a test, you'll either (a) ship nothing because the eval regressed by 1%, or (b) ignore every eval because you can't block on them.

What they're actually useful for

  • Regression detection over time. Run evals on every release candidate. Watch the trend.
  • Model comparison. Which checkpoint is better? Evals are the right tool.
  • Rubric documentation. Writing an eval forces you to define "good output" in a way stakeholders can review.

What they can't do

  • Catch rare catastrophic failures. Use guardrails and production monitoring for that.
  • Replace human review for high-stakes domains.
  • Tell you why something regressed. Trace the eval back to specific examples — that's where the signal lives.

Treating evals as measurement instruments rather than gatekeepers is the unlock. Your team's throughput will thank you.

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