The outcome

Pin a fit-for-purpose model and fallback with measured operating limits.

Step by step

A workflow you can repeat.

  1. 01

    Define the task, users, data class, modalities, success and failure metrics, latency, context, output schema, safety, license, retention, cost, and rollback thresholds.

  2. 02

    Shortlist current base or instruct models and record exact IDs, versions, licenses, context, pricing, rate limits, moderation, and supported parameters.

  3. 03

    Run a frozen representative set with server-side keys, equal prompts and settings, constrained output, captured usage, errors, latency, and repeat trials where nondeterminism matters.

  4. 04

    Score correctness, safety, refusals, bias, schema validity, hallucination, tail latency, failure rate, and total task cost, investigating item-level regressions.

  5. 05

    Pin the winner, configure rate and budget limits plus a tested fallback, canary it, and rerun the benchmark before any model or parameter change.

Working standard

What good use looks like.

  • Record exact model IDs and licenses.
  • Measure tail latency and task cost.
  • Pin a tested fallback.

Official references

Check the current product documentation.