AI Engineering from Scratch（英文原版）
Given the feature change (prompt / model / generation parameter), baseline metrics, expected lift, and team posture (warehouse-native OSS vs bundled SaaS), produce an A/B plan.
Produce:
- Platform. Statsig (bundled SaaS, OpenAI-owned) or GrowthBook (MIT OSS, warehouse-native). Justify.
- Primary metric + guardrails. Primary is the metric you are trying to move; guardrails are things that must not regress (cost/request, latency P99, refusal rate).
- Sample size. Classical power calculation × 1.4 (LLM non-determinism buffer).
- Design. Fixed-horizon or sequential. Sequential if you expect strong signals; fixed if the change is subtle.
- CUPED. Enable if pre-period data exists for the primary metric; specify the regressor.
- Correction. Bonferroni for small number of tests; Benjamini-Hochberg for many related tests.
- SRM. Require SRM check on every experiment; halt and debug if flagged.
Hard rejects:
- Shipping on vibes. Refuse — require A/B or documented no-A/B exception.
- Running >5 experiments on the same primary metric without BH/Bonferroni. Refuse — false discovery certain.
- Skipping SRM check. Refuse — assignment bugs are common.
Refusal rules:
- If traffic < 1000 users/week for the feature, refuse fixed A/B — require shadow + canary (Phase 17 · 20) instead.
- If the primary metric is subjective (e.g., "quality") without an objective proxy, require human eval in parallel.
- If the lift hypothesis is smaller than the LLM noise floor, refuse — the experiment cannot detect it with realistic sample size.
Output: a one-page plan with platform, primary + guardrails, sample size, design, CUPED, correction, SRM policy. End with the decision rule: primary significant + all guardrails not significant-negative → ship; any guardrail breach → do not ship regardless of primary.