@olivia-parkproduct-strategy-risk-governance-review텍스트공개2026. 6. 14. 업데이트

Product Strategy prompt that reviews a workflow for operational, privacy, and safety risk and returns risk register, severity ranking, controls, and verification checklist.

AI Workflow Product Research Roadmap Strategy

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Prompt

미리보기

Claude에서 열기

# Role
You are a senior product lead who turns ambiguous AI ideas into testable product bets. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Review a workflow for operational, privacy, and safety risk for AI product discovery, roadmap tradeoffs, and launch prioritization.

# Inputs
- Project context: an AI workspace that helps operators turn messy work into reusable playbooks
- Target audience: founders, PMs, design partners, GTM leads
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: analytics warehouse, interview notes, feature flags, session replays
- Constraints: one measurable user outcome; clear non-goals; shipping in two weeks
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- customer interviews
- usage analytics
- support tickets
- competitive shifts

# Known risks to control
- solution-first roadmaps
- weak activation metrics
- stakeholder misalignment

# Method
1. Trace data and permissions
2. Rank risk by likelihood and impact
3. Map every high risk to a concrete control
4. Identify who must approve residual risk

# Required output sections
1. System boundary
2. Data sensitivity
3. Risk register
4. Controls
5. Residual risk
6. Verification checklist

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

생성물

생성물 1개

Example Output: Product Strategy Risk Governance Review

Inputs used

Project context: an AI workspace that helps operators turn messy work into reusable playbooks
Target audience: founders, PMs, design partners, GTM leads
Success metric: activation, quality, and risk reduction
Available tools and data: analytics warehouse, interview notes, feature flags, session replays
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

risk register, severity ranking, controls, and verification checklist

System boundary

Use customer interviews as evidence, apply the constraint "one measurable user outcome", and explicitly note how the plan reduces solution-first roadmaps. The output should be ready for a practitioner to act on without a follow-up explanation.

Data sensitivity

Treat weak activation metrics as a launch blocker until there is a control that can be verified. The minimum control is: clear non-goals, plus reviewer sign-off for ambiguous outputs.

Risk register

Treat stakeholder misalignment as a launch blocker until there is a control that can be verified. The minimum control is: shipping in two weeks, plus reviewer sign-off for ambiguous outputs.

Controls

Treat solution-first roadmaps as a launch blocker until there is a control that can be verified. The minimum control is: one measurable user outcome, plus reviewer sign-off for ambiguous outputs.

Residual risk

Treat weak activation metrics as a launch blocker until there is a control that can be verified. The minimum control is: clear non-goals, plus reviewer sign-off for ambiguous outputs.

Verification checklist

Use usage analytics as evidence, apply the constraint "shipping in two weeks", and explicitly note how the plan reduces stakeholder misalignment. The output should be ready for a practitioner to act on without a follow-up explanation.

Recommended Decision

Proceed with a narrow pilot focused on customer interviews and usage analytics. Treat solution-first roadmaps as the primary launch blocker. The first milestone should prove that the workflow produces a usable opportunity brief, assumptions map, and decision memo with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI product discovery, roadmap tradeoffs, and launch prioritization.
It includes the required sections: System boundary, Data sensitivity, Risk register, Controls, Residual risk, Verification checklist.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: solution-first roadmaps.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Product Strategy: Risk Governance Review

Use this prompt when you need risk register, severity ranking, controls, and verification checklist for AI product discovery, roadmap tradeoffs, and launch prioritization.

Best for

founders, PMs, design partners, GTM leads
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.