In brief
- Set a task-specific quality gate.
- Test failures and abuse paths.
- Canary with monitoring and a rollback plan.
Write the feature contract
Define supported tasks, users, data, latency target, quality threshold, fallback behavior, and what the product says when it cannot complete the task. This contract keeps evaluation and UX aligned.
Complete the preflight evidence
Run task evaluations, adversarial cases, load checks, dependency failure drills, privacy review, and cost estimates at expected and worst-case usage.
- Representative quality evaluation
- Timeout and retry tests
- Authorization and prompt-injection review
- Usage and spend ceilings
- Support and incident runbook
Release progressively
Start with internal users or a small percentage, compare observed behavior to the preflight, and expand only when both technical and task-success metrics remain inside bounds.
Make rollback boring
Keep a feature flag or configuration switch, preserve the previous stable path, and define who can roll back. Test the switch before launch rather than during the incident.
Do not call the release complete while the production smoke check, monitoring, or rollback path remains unverified.
Frequently asked
Questions, answered plainly.
Is a successful staging test enough?+
No. Staging cannot fully reproduce traffic, data shape, provider state, or user behavior. Use a monitored production canary.
What is the most important AI launch metric?+
The task-specific user outcome is primary, supported by reliability, latency, cost, and safety metrics.
When should a launch roll back?+
Predefine thresholds for severe quality failures, security issues, error rate, latency, spend, and unavailable recovery paths.
Sources and next paths