Use when converting an approved AI feature strategy into testable PRD requirements for quality, latency, cost, abstention, citations, consent, and evaluation; use ai-feature-strategy-doc for portfolio choices.
Use when converting an approved AI feature strategy into testable PRD requirements for quality, latency, cost, abstention, citations, consent, and evaluation; use ai-feature-strategy-doc for portfolio choices.
converting an approved AI feature strategy into testable PRD requirements for quality, latency, cost, abstention, citations, consent, and evaluation; use ai-feature-strategy-doc for portfolio choices.
Use this procedure when the required source artefacts are available and AI feature PRD specification is the next lifecycle deliverable.
Do Not Use When
Use ai-feature-strategy-doc when that neighbouring route owns the decision or deliverable.
Do not invent missing project evidence, standards clauses, thresholds, or stakeholder decisions.
Required Inputs
Artefact
Source or provider
Required?
Behaviour when missing
Approved AI feature strategy, users, data constraints, and evaluation targets
Product owner and AI governance owner
Yes
Stop the affected step, name the missing source, and return only a qualified gap record.
Workflow
Inspect the required inputs and log the exact sources, versions, and unresolved assumptions.
Apply this skill's existing domain workflow and decision rules to produce AI feature PRD specification.
Stop when a required source, accountable decision owner, or deterministic test oracle is absent.
Recover by preserving valid work, marking the blocked scope, and returning the narrowest qualified artefact plus the next evidence needed.
Outputs
Artefact
Consumer
Acceptance condition
AI feature PRD specification
AI architecture, evaluation, safety, and delivery teams
Required sections are populated, source links resolve, and every material requirement or decision has an observable review or test oracle.
Evidence Produced
Evidence
Reviewer
Acceptance condition
Source, decision, trace, and validation record for AI feature PRD specification
Requirements quality reviewer
Inputs used, decisions made, checks run, failures, and unassessed items are explicit.
Capability and permission boundaries
Read and search are required. Editing is allowed only when the request authorises creation or repair of the named requirements artefact. Publishing, production mutation, destructive action, spending, and certification require explicit authority.
Degraded mode
Fallback: if a required file, reviewer, standard source, network check, renderer, or execution capability is unavailable, return the narrowest useful qualified result and mark the affected check not assessed; never convert an unassessed check into a pass.
Decision Rules
Choice or condition
Action
Failure or risk avoided
A model behaviour has no measurable evaluation oracle
Mark it blocked and define the dataset, metric, threshold, and owner.
An AI claim that cannot be tested or governed.
Required inputs and test oracles are complete
Continue through the existing workflow and record evidence.
A deliverable whose acceptance cannot be reproduced.
A mandatory source or owner is missing
Stop the affected branch and issue a qualified gap record.
Fabricated context or unauthorised decisions.
Quality Standards
Preserve stable identifiers and bidirectional traceability from project evidence to AI feature PRD specification and its acceptance checks.
Apply ISO/IEEE measures only with a named metric, method, threshold, evidence source, and responsible reviewer; run the anti-slop gate before release.
Anti-Patterns
Producing AI feature PRD specification from assumed context. Fix: cite the project source or mark the scope blocked.
Accepting a material requirement without a deterministic oracle. Fix: add a measurable result, boundary, and verification method.
Crossing into ai-feature-strategy-doc without routing the decision. Fix: hand off the named input and preserve trace links.
Treating an unavailable check as passed. Fix: mark it not assessed and state the release consequence.
Claiming standards, statutory, or stakeholder approval without evidence. Fix: cite the source and reviewer or qualify the claim.
Produces the AI-feature complement to the generic PRD. Every AI-powered FR carries seven mandatory clauses that the generic PRD does not collect. The acceptance gates point at the eval harness as the test oracle.
List every FR whose output is produced or modified by an AI component (LLM call, RAG, classifier, embedding search, agent action, fine-tune).
Step 2: Attach the seven AI clauses to each FR
Also attach the model/system/input/output map, human oversight, correction,
contest, undo or safe fallback, consent/notice, drift signal, and rollback
acceptance evidence from references/ai-system-human-control-contract.md.
For each AI-powered FR, the spec MUST capture:
Clause
Form
Example
Hallucination tolerance
factuality score threshold
factuality >= 0.92 on golden set; abstain otherwise
Latency budget
P95 target ms
P95 <= 2000 ms; timeout at 8000 ms with graceful fallback
$/call ceiling
USD or token cap
<= $0.04/call; throttle when tenant > $X/day
Abstain criteria
rule
abstain when retrieval returns < 2 relevant chunks OR confidence < 0.6
Citation policy
rule
every claim about ingested document cites the source span
Consent / opt-in
rule
feature is opt-in per workspace admin; default off for EEA tenants
Training-data exclusion
rule
tenant content is not used to train the provider model; provider's no-training endpoint used
Step 3: Define structured output requirements
Where feasible the output is structured (JSON schema, function-call payload, enum). Free-form prose is reserved for user-visible text that has a separate guard (length, style, banned-terms list).
Step 4: Define safety and content rules
State which content policy applies (no medical advice, no legal advice, no investment advice, no PII generation, no protected-class judgements). Cite the safety harness scenarios that verify each rule.
Step 5: Define human-in-the-loop / contestability
Which decisions require human approval before commitment. How a user contests an output (button, reroute, escalation). Reference EU AI Act Art. 14 obligations where the feature is high-risk.
Step 6: Define rollout posture per FR
Initial rollout (canary cohort, opt-in beta, free-tier first), promotion gates (eval pass, red-team pass, hallucination SLO met for N days), and the rollback trigger.
Step 7: Define acceptance tests against the eval harness
Every AI FR has a row in the eval harness golden set with a pass threshold. Acceptance = "harness green for 30 d on this case set + red-team pass for the corresponding adversarial set".
Step 8: Write the spec
AI_Feature_PRD_Spec.md sections: 1) AI FR Inventory, 2) Per-FR AI Clauses, 3) Structured Output Requirements, 4) Safety & Content Rules, 5) Human-in-the-Loop & Contestability, 6) Rollout Posture, 7) Eval Acceptance Gates, 8) Traceability to PRD and to eval harness IDs.
Standards
IEEE 830-1998
NIST AI RMF MAP / MEASURE
EU AI Act Art. 13 (transparency), Art. 14 (human oversight)
