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Create a scorer

A scorer turns evaluation evidence into a score or decision. Start with the cheapest deterministic check that captures the requirement, and add model-backed judgment only where semantic interpretation earns its cost.

Pick the right kind

Step 1 — Open Measurement → Scorers

Open /measurement?view=scorers and choose New scorer. The old /scorers list route redirects into this Measurement view. Existing scorer detail links such as /scorers/{scorerId} remain valid.

Step 2 — Define the scorer

Choose the scorer kind and configure the behavior it owns. Keep model/provider, prompt, thresholds, mappings, examples, and other decision inputs versioned where the scorer type exposes them. Code-backed execution is governed by the scorer runtime boundary; do not assume your local Node or Python environment is identical to production execution.

Step 3 — Add and review examples

Add labeled examples that represent both acceptable behavior and important failures. Examples are measurement evidence, not a substitute for independent qualification.

Step 4 — Test and qualify

Run the scorer against its examples and inspect disagreements. Where the workflow requires evaluator qualification, complete that evidence before using the scorer as release authority.

Step 5 — Approve, publish, and bind the exact version

Publishing/binding uses immutable scorer versions. A later edit creates new evidence; it does not silently rewrite the version used by an earlier run.

Up next

Calibrate the scorer when scores need cross-version or cross-model comparability.