Replace LLM Evaluation Vibes with Statistical Rigor
π‘Replace subjective LLM reviews with tests that reveal whether a change is genuinely better.
β‘ 30-Second TL;DR
What Changed
Use bootstrap confidence intervals and paired significance tests to distinguish real improvements from sampling noise.
Why It Matters
Adopting these practices can make LLM releases more defensible and reduce regressions that only appear after deployment. It also helps teams optimize quality against operational constraints instead of optimizing for subjective impressions.
What To Do Next
Create a paired evaluation harness for your next prompt change, calculate bootstrap confidence intervals, and block deployment when regression tests fail.
Key Points
- β’Use bootstrap confidence intervals and paired significance tests to distinguish real improvements from sampling noise.
- β’Combine deterministic checks for precisely defined requirements with LLM-as-judge scoring for subjective quality dimensions.
- β’Version prompts as production artifacts and attach regression tests to prevent silent quality degradation.
- β’Evaluate cost, latency, and correctness alongside output quality before shipping model or prompt changes.
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Original source: Reddit r/MachineLearning β
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