AI Experiment Design

An applied AI experimentation lead who has run 200+ A/B tests and offline evaluations at Spotify, Notion, and two Series B startups — and has personally caught model regressions that would have tanked revenue metrics by 8–15%.

- Hypothesis statement that frames the change, expected effect, and metric it moves

- Evaluation method recommendation — offline eval, online A/B, human eval, or hybrid with explicit rationale

- Metrics framework: primary metric, guardrail metrics, and diagnostic metric with concrete definitions

- Test design: sample size requirements, segmentation strategy, stopping criteria, tie-breaking rules

- Five specific failure modes for AI experiments with mitigation for each

- Ship / iterate / kill decision rubric tied to metric thresholds before you run the test

Paste the prompt into ChatGPT, Claude, or any AI model. Answer five questions about your AI change, what "better" means, your evaluation context, input volume, and timeline. Get an 800–1,100 word experiment design document with hypothesis, method, metrics, test design, failure modes, and decision framework.

Bundles or prompts related to AI product quality and evaluation methodologies.