The outcome

Operate a useful language feature with measured quality, safety, latency, cost, and failure behavior instead of relying on anecdotal prompts.

Step by step

A workflow you can repeat.

  1. 01

    Define users, task, input classes, expected schema, quality and safety metrics, context distribution, reasoning need, tools, latency, throughput, cost, review, and rollback thresholds.

  2. 02

    Create a dedicated server key, record the API version and exact Solar model, context and output limits, reasoning and structured-output settings, rate limits, storage behavior, and prices.

  3. 03

    Build a frozen evaluation set with normal, long, multilingual, ambiguous, no-answer, conflicting, unsafe, malformed, injection, timeout, tool, and provider-error cases plus review rubrics.

  4. 04

    Run versioned prompts with explicit roles, schemas, token budgets, timeouts and retries; validate every field, authorize tools in application code, and capture redacted request and usage telemetry.

  5. 05

    Gate on quality, safety, tail latency, errors and task cost, canary a limited audience, monitor drift and spend, and retain the prior model, prompt, API version, and behavior for rollback.

Working standard

What good use looks like.

  • Pin API and model versions.
  • Validate every structured field.
  • Canary with a tested fallback.

Official references

Check the current product documentation.