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Write a PRD for an AI-powered feature, covering the things normal PRDs miss.
Passed 17 of 22 test cases with the skill, and 5 of 22 without it.
and 16 more in the full report
13 fixed4 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write an AI feature PRD for an automated executive commentary generator attached to quarterly financial tables. The stakes for misstated numbers are high.
| Checked | Without skill | With skill |
|---|---|---|
| Section 4 establishes an explicit ship threshold and error rate calibrated to high-stakes financial data | Missed | Passed |
The instructions your model reads when the skill loads.
AI features break the normal PRD because the system is probabilistic: it will be wrong sometimes, and the product must be designed around that, not in denial of it. This skill extends a standard PRD with the AI-specific sections that decide whether the feature is trustworthy — the UX of uncertainty, the eval bar, guardrails, and what happens when the model is wrong.
Ask for these only if they aren't already provided:
1. Problem & why AI — the user problem, and why a model (not rules) is the right tool. If rules would do, say so.
2. Experience — the core flow, and crucially the UX of uncertainty: how confidence is shown, how the user verifies/edits, and how errors are made cheap to recover from. AI features live or die here.
3. Model approach — prompt / fine-tune / RAG / agent (link rag-design-doc or agent-spec), the model tier, and why.
4. Quality bar & evaluation — the metrics and the explicit ship threshold; reference an ai-eval-plan. State the acceptable error rate given the stakes.
5. Guardrails & safety — what the feature must never do, input/output filtering, and handling of harmful/PII/out-of-scope inputs.
6. Fallback behaviour — what happens when the model is unsure, wrong, slow, or down: graceful degradation, "I'm not sure" states, human handoff. No silent confident errors.
7. Data flywheel — how usage (and the 👍/👎 / edits) feed back into evaluation and improvement, with the privacy boundary.
8. Cost & latency — the per-request budget and p95 target; reference an llm-cost-latency-budget.
9. Rollout — staged exposure (internal → %→ GA), the guardrail metrics watched, and the rollback trigger.
test cases passed, vs 5 / 22 without
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