ai-feature-prd
AI Feature PRD Expert
Overview
AI and ML features break the assumptions a standard PRD takes for granted. Outputs are non-deterministic. Quality is statistical, not categorical. The "spec" is half product, half eval suite. A regular PRD that says "Search returns the top result" is replaced by "the assistant returns a helpful, harmless, on-policy answer with a refusal rate under 4% on the golden set, p95 latency under 1.8s, and cost-per-conversation under $0.05."
This skill produces an AI Feature PRD that extends the standard 8-section PRD (see create-prd/) with three additional sections built for the realities of shipping AI: AI System Design (Section 9), Eval & Safety Plan (Section 10), and Operations & Cost (Section 11). It draws on Karpathy's "Software 2.0" framing (the model is the spec), Anthropic's Responsible Scaling Policy patterns, the OpenAI Model Spec style for defining intended behavior, prompt-first architecture discipline, and the EU AI Act's risk-tier model. This is a template-based skill -- no Python tool; the artifact is a markdown PRD. Pair this with engineering/llm-cost-optimizer/ for the cost-model math and with ra-qm-team/eu-ai-act-specialist/ for the regulatory classification.
Core Capabilities
- 11-section AI PRD — the standard 8-section spine plus AI System Design, Eval & Safety Plan, and Operations & Cost.
- Model & architecture decisions — primary/fallback/switch logic; prompt vs few-shot vs RAG vs fine-tune vs agent selection with rejected-alternative rationale; data flow and prompt contract.
- Eval & safety planning — golden sets, acceptance/hallucination/refusal/latency/cost metrics, guardrail layers, refusal policy, failure-mode taxonomy, human-in-the-loop gates, ethical review.
- Operations & cost — cost model, per-tenant metering, shadow→internal→canary→percent→GA deployment ramp with gates, and lifecycle/prompt versioning.