Llm As Judge Scorer

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: imtiazrayhan
• Source: imtiazrayhan/agentscamp-library
• License: MIT
• Homepage: https://agentscamp.com

Install and usage instructions live in the source repository linked above.