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.

When to Use

• Adding an AI/ML feature to an existing product -- a search assistant, a recommendation, an auto-summarizer, a copilot, an agent.
• Building an AI-first product -- the entire surface area is model-mediated.
• Migrating a deterministic feature to an LLM -- replacing a rules-based system or scripted flow with a model.
• Fine-tuning, prompt-tuning, or RAG decision -- the PRD captures the architecture rationale so engineering does not relitigate it mid-build.
• Regulated context -- EU AI Act, HIPAA, FINRA, FDA SaMD -- the PRD must enumerate the risk tier, the eval bar, and the audit trail before kickoff.

When NOT to use: for a non-AI feature (use create-prd/); for pure model R&D with no product surface (use a research design doc); for a one-off internal prompt or batch script that does not ship to users (a Notion page is fine); when the AI feature has no production traffic plan.

Clarify First

Before drafting the AI PRD, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• AI task & surface — what the model does and where it appears to the user (drives Section 9 model selection + architecture pattern: prompt vs RAG vs fine-tune vs agent)
• Quality & safety bar — the acceptance / hallucination / refusal / latency targets that define "good enough" (drives Section 10's eval criteria and golden set)
• Risk / regulatory tier — EU AI Act tier or regulated context (health, finance, legal) (drives Section 10.7 ethical review and where human-in-the-loop is mandatory)
• Cost & traffic envelope — expected volume and cost-per-call ceiling (drives Section 11's cost model and the deployment ramp gates)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.