Rollout Guide
AI Experiment Tracker Playbook for AI Product Teams
AI experiments fail to generate learning when results are not documented with hypothesis and metric context. This playbook defines an experiment tracking workflow with decision gates.
Implementation Steps
- Document hypothesis, expected metric change, and rollback criteria before launching.
- Track daily metric movement with confidence intervals and sample size.
- Set decision gates: deploy, extend, rollback based on statistical thresholds.
- Archive completed experiments with learning summary and follow-up actions.
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- Provider and model split recommendations
- Budget guardrail design by traffic stage
- KPI plan for spend, quality, and conversion