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AutoPinFlow AI • Automation • Future Technology

Your Evals Are Lying: Building Honest Benchmarks for LLM Features

Benchmark contamination, reviewer bias and metric drift all inflate scores. A practical framework for evaluations you can actually trust.

Contamination is the default

If your test set is public, assume it is in training data. Private, freshly written tasks are the only reliable signal.

Score what users feel

Latency, refusal rate and recovery from bad input matter more to perceived quality than a two-point accuracy delta.

PN

Priya Nair

ML Correspondent

Priya translates machine learning research into practical guidance for engineering teams.

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