01 / Build a test set that resembles the work
Gather representative examples with expected labels. Include infrequent classes, ambiguous inputs, and known failure cases. Keep tuning examples separate from the final held-out test set.
Agree on label definitions before scoring. If two people consistently disagree, clarify the task or introduce a review outcome instead of treating every disagreement as a model failure.
02 / Measure what a mistake costs
| Measure | What to record |
|---|---|
| Decision quality | Per-class precision and recall, confusion matrix, and error examples. |
| Latency | Cold start, warm p50 and p95, concurrency, and end-to-end request time. |
| Reliability | Failed requests, retries, timeouts, and invalid outputs. |
| Economics | Service or compute costs, plus the work required to operate each path. |
| Escalation | Share sent to review and residual errors in automatically handled cases. |
03 / Keep conditions visible
Use identical inputs and task definitions. Save raw outputs and timings. Record model and package versions, hardware, region, batch size, and concurrency. Do not compare a local warm run with a remote cold request without labeling the difference.
04 / Set the acceptance criteria before the test
Decide which failures are unacceptable and how much review work the team can support. Select confidence thresholds using validation data, then evaluate the resulting policy on held-out examples.
Ship a small, monitored rollout with a fallback and a way to revert. A model that wins on a static dataset still needs observation when the input distribution changes.
This page is an editorial evaluation framework. It reports no measured model scores.
Sources & review notes
Source review: September 27, 2026. Product documentation can change. Atlas has not rerun model inference. Third-party measurements, where included, are attributed to their authors.
- Laya repository — candidate implementation and model documentation
- TypeSafe AI — candidate hosted service