AI Feature PRD Skill

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.

Required Inputs

Ask for these only if they aren't already provided:

• The user problem and why an AI/probabilistic approach fits it (vs. deterministic rules).
• What "good" looks like to the user, and the cost of a wrong answer (low-stakes vs. high-stakes).
• Inputs available — context/data the model can use; privacy constraints.
• Trust level needed — can the user verify the output, or must it be near-perfect?

Reads from / Writes to the Brain

If a professional-brain exists, read context.md (product, users, voice) and knowledge/strategy.md first; write the feature to entities/ and any scoping decision to decisions/, each provenance-tagged.

Output Format

AI Feature PRD: [feature]

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.

Quality Checks

• The PRD designs for the model being wrong — there's an explicit fallback, not just the happy path
• The UX shows uncertainty and lets the user verify/correct cheaply
• There's an explicit quality bar tied to the stakes (a medical answer and a tweet draft are not the same bar)
• Guardrails name what the feature must never do
• A data flywheel is defined with its privacy boundary
• Cost and p95 latency budgets are stated, not left to "we'll see"

Anti-Patterns

• Do not design only the happy path — a probabilistic feature without a fallback is a feature that fails loudly in production
• Do not hide uncertainty behind a confident UI — overclaimed confidence is how AI features lose user trust permanently
• Do not use AI where deterministic rules are better, cheaper, and more reliable — "AI" is not the goal
• Do not set one quality bar for all stakes — calibrate the acceptable error rate to the cost of being wrong
• Do not ship without a rollback trigger and guardrail metrics — a probabilistic system needs a kill switch

Based On

Standard PRD practice (see prd-template) extended for probabilistic systems — uncertainty UX, eval gates, guardrails, and graceful fallback.

Example Trigger Phrases

• "Spec an AI/LLM feature."
• "Write a PRD for a feature that uses a model."
• "Plan an AI capability."