Is an LLM judge good enough, or do you need human evaluation?
An LLM judge is good enough for continuous scoring, but only once it has been calibrated against human labels. The humans establish the ruler; the model then reads it thousands of times a day at a cost no human process can match. A judge that has never been checked against human agreement is an unvalidated instrument.
The calibration loop is simple: hand-label a few hundred outputs, run the judge over the same set, and measure agreement. If agreement is below roughly 0.7, the rubric is ambiguous rather than the judge being weak — rewrite the rubric into concrete, checkable criteria and try again.
Re-run that check whenever the judge model version changes. Judge behaviour moves with model updates, and an uncalibrated judge drifting under you looks exactly like your product improving or degrading.
Keep humans in the loop on a sample rather than the whole stream. Reviewing 1–2% of production traffic weekly is usually enough to notice when the judge and reality have come apart.
Written by Binary AI Labs · Reviewed