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.

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.

References

Pull the reference that matches the task; keep this file lean and load detail on demand.

• references/ai-prd-structure.md — why a separate AI PRD is needed (standard-vs-AI comparison), the full 11-section framework with every sub-section (model selection, architecture pattern, data flow, prompt contract, eval criteria, golden set, guardrails, refusal policy, failure modes, HIL, ethical checklist, cost model, deployment ramp, lifecycle), the authoring workflow, tools/assets, troubleshooting, and success criteria. Read when drafting any AI Feature PRD.
• references/ai-pm-frameworks-guide.md -- Software 2.0 (Karpathy), OpenAI Model Spec, Anthropic RSP, Reforge AI PM, EU AI Act tiers, and the AI PM playbook. Read for the conceptual grounding behind the PRD sections.
• references/eval-design-guide.md -- golden sets, pairwise eval, RAGAS, Promptfoo, Langfuse, online vs offline eval, drift detection. Read when designing Section 10's eval suite.
• references/red-flags.md -- concrete examples of how AI PRDs go wrong and how to fix them. Read when reviewing a draft for quality.
• assets/ai_feature_prd_template.md -- full 11-section AI PRD template. Use to author the artifact.
• assets/eval_spec_template.md -- eval contract: golden set, metrics, cadence, owners.
• assets/guardrail_checklist.md -- input/output/HIL guardrail walkthrough.
• assets/failure_mode_taxonomy.md -- AI-specific failure mode catalogue and mitigations.

Scope & Limitations

In Scope: the 11-section AI Feature PRD template (model selection with primary/fallback/switch logic, eval criteria with golden set + hallucination/refusal/latency/cost metrics, guardrail layers, failure-mode taxonomy, deployment ramp with gates, cost model + per-tenant metering, ethical review with EU AI Act tier declaration).

Out of Scope: building/running evals (use Promptfoo, Langfuse, Anthropic Console, Braintrust); cost-model arithmetic (use engineering/llm-cost-optimizer/); regulatory classification deep dive (use ra-qm-team/eu-ai-act-specialist/, ra-qm-team/iso42001-ai-management/); standard PRD structure for non-AI features (use create-prd/); detailed system architecture (engineering RFC); production model training pipelines (MLOps tooling).

Important Caveats: model versions move fast — re-evaluate the primary every 90 days and design the PRD so a model swap is a controlled change, not a rewrite. A "100% acceptance" target means the golden set is too easy (real features land at 85-95% on hard tasks). Cost projections at low traffic underestimate real spend — model a 10x scenario before launch. Treat refusal policy as living guidance. AI features in regulated industries (health, finance, legal) require human-in-the-loop on every high-stakes action.

Integration Points