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, the Reforge AI PM curriculum, 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.