When output is open-ended — a summary, a support answer, tone, helpfulness — you can't score it with exact match, and human grading doesn't scale. An LLM-as-judge does, but only if it's built carefully: an uncalibrated judge produces confident, inconsistent scores that quietly corrupt every downstream decision. This skill designs a judge you can actually trust.
When to use this skill
- Grading subjective or open-ended outputs where there's no single correct string.
- Replacing slow, inconsistent manual review in an eval loop.
- An existing LLM-as-judge gives scores that don't match your own judgment.
Instructions
- Define the rubric explicitly. State precisely what's being judged and the criteria. Vague instructions ("rate quality 1–10") produce noise; concrete criteria ("deduct if the answer omits the rotation step, hallucinates a flag, or exceeds 3 sentences") produce signal.
- Use a discrete scale with anchors. Prefer a small scale (e.g. pass/fail or 1–5) with a written description of what each level means. Discrete, anchored scales are far more consistent than a bare 1–10.
- Provide reference examples. Include a few scored examples in the judge prompt — especially boundary cases — so the model calibrates to your standard rather than its own.
- Control known biases. LLM judges favor longer answers, their own model family's style, and the first option in a pairwise test. Mitigate: randomize order in pairwise comparisons, instruct length-neutrality, and consider a different model as judge than the one under test.
- Validate against human labels. Hand-label 20–30 cases, run the judge, and measure agreement. If the judge disagrees with you often, fix the rubric — do not deploy a judge you haven't checked against ground truth.
- Wire it in. Implement as a custom metric in your framework (e.g. DeepEval's G-Eval or a custom scorer) and add it to the suite with a threshold.
[!WARNING]
An LLM judge you haven't validated against human labels is not a metric — it's an opinion with a number attached. Calibrate before you trust it, and re-check when you change the judge model.
[!NOTE]
Where possible, prefer a deterministic check (schema validity, exact match, a regex) over an LLM judge — it's cheaper, faster, and perfectly consistent. Reserve the judge for what genuinely needs judgment.
Output
A validated judge: the rubric and scale, reference examples, the bias controls applied, the human-agreement score, and the metric wired into the eval suite.
1---2name: llm-as-judge-scorer3description: Design a reliable LLM-as-judge metric — a calibrated rubric, a clear scoring scale, and bias controls — and validate it against human labels before trusting it. Use when grading open-ended LLM output (summaries, answers, tone) that exact-match can't score.4---56When output is open-ended — a summary, a support answer, tone, helpfulness — you can't score it with exact match, and human grading doesn't scale. An **LLM-as-judge** does, but only if it's built carefully: an uncalibrated judge produces confident, inconsistent scores that quietly corrupt every downstream decision. This skill designs a judge you can actually trust.78## When to use this skill910- Grading subjective or open-ended outputs where there's no single correct string.11- Replacing slow, inconsistent manual review in an eval loop.12- An existing LLM-as-judge gives scores that don't match your own judgment.1314## Instructions15161. **Define the rubric explicitly.** State precisely what's being judged and the criteria. Vague instructions ("rate quality 1–10") produce noise; concrete criteria ("deduct if the answer omits the rotation step, hallucinates a flag, or exceeds 3 sentences") produce signal.172. **Use a discrete scale with anchors.** Prefer a small scale (e.g. pass/fail or 1–5) with a written description of what each level means. Discrete, anchored scales are far more consistent than a bare 1–10.183. **Provide reference examples.** Include a few scored examples in the judge prompt — especially boundary cases — so the model calibrates to your standard rather than its own.194. **Control known biases.** LLM judges favor longer answers, their own model family's style, and the first option in a pairwise test. Mitigate: randomize order in pairwise comparisons, instruct length-neutrality, and consider a different model as judge than the one under test.205. **Validate against human labels.** Hand-label 20–30 cases, run the judge, and measure agreement. If the judge disagrees with you often, fix the rubric — do not deploy a judge you haven't checked against ground truth.216. **Wire it in.** Implement as a custom metric in your framework (e.g. DeepEval's G-Eval or a custom scorer) and add it to the suite with a threshold.2223> [!WARNING]24> An LLM judge you haven't validated against human labels is not a metric — it's an opinion with a number attached. Calibrate before you trust it, and re-check when you change the judge model.2526> [!NOTE]27> Where possible, prefer a deterministic check (schema validity, exact match, a regex) over an LLM judge — it's cheaper, faster, and perfectly consistent. Reserve the judge for what genuinely needs judgment.2829## Output3031A validated judge: the rubric and scale, reference examples, the bias controls applied, the human-agreement score, and the metric wired into the eval suite.
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