Shipping AI features needs more than a demo that works once. Before launch, align on a release checklist that balances speed and stability.
1. Define quality gates before implementation
Agree on success metrics early:
- Response quality thresholds
- Acceptable latency ranges
- Maximum cost per successful task
If quality gates are unclear, teams optimize for the wrong thing.
2. Validate the failure modes
Design for what happens when the model fails:
- Empty or low-confidence output
- Incorrect assumptions
- Safety or policy violations
Fallback behavior should be explicit and testable.
3. Add observability by default
Track every release with:
- Prompt and model version identifiers
- Outcome labels for quality review
- Segment-level performance trends
Observability turns incidents into quick fixes instead of long investigations.