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How to check a judge's accuracy before it blocks a release with Tracely AI

Label judge verdicts against human review to catch over-flagging before it gates a PR.

Who it is forA QA or ML engineer who has added an LLM judge to a release gate and wants proof it flags the right runs.

The problemAn LLM judge that over-flags will block good pull requests, and an under-flagging judge lets regressions ship.

The outcomeEach judge gets an agreement score against human labels, with missed failures and over-flagging shown separately.

What you need first

  • Docker and Docker Compose
  • For local development: uv, Node 20+ and pnpm
  • A GitHub repository with pull requests, if you want the CI gate
  • Python, to install the tracelyai-sdk package

Steps

  1. Run docker compose --profile demo up -d --build --wait and open http://localhost:3001.
  2. Open the calibration screen.
  3. Label a set of judge verdicts as pass or fail based on your own review.
  4. Read the per-evaluator agreement, missed failures and over-flagging counts.
  5. Only attach a judge to the CI gate if its agreement is acceptable to your team.

Prompt for your agent

In the Tracely-ai repository, run `docker compose --profile demo up -d --build --wait` and open the calibration screen at http://localhost:3001. Label at least ten judge verdicts and report each evaluator's agreement, missed failures and over-flagging. Verify that the three numbers are shown for every evaluator you labeled.

Caveat. Calibration is only as good as the human labels you provide, so the agreement numbers reflect your reviewers' judgment as much as the judge.

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