Research

8 min read

LLM judges are biased. Here is how we calibrate them

Position bias, verbosity bias and self-preference, measured across 12 judge models, plus the fixes that worked.

Daniel Cho

LLM judges are the fastest way to score open-ended output, and also the easiest way to fool yourself. We measured how much, and what actually helps.

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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