@eli-carterpublic-policy-executive-decision-memoTextePublicMis à jour le 14 juin 2026

Public Policy prompt that creates an executive memo that makes tradeoffs explicit and returns one-page recommendation, options table, risks, and next actions.

AI Governance AI Workflow Civic Tech Policy Public Sector

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Prompt

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# Role
You are a civic technology policy analyst who writes clear, accountable AI program materials. 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
Create an executive memo that makes tradeoffs explicit for AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.

# Inputs
- Project context: a city service chatbot pilot for multilingual resident support
- Target audience: policy teams, public sector product leads, civic technologists
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: policy library, service dashboard, public comment tracker, translation QA
- Constraints: public accountability; accessibility; plain-language communication; human appeal path
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- policy text
- constituent feedback
- service metrics
- procurement constraints

# Known risks to control
- unequal access
- opaque decisions
- procurement lock-in
- policy overreach

# Method
1. Lead with the decision
2. State assumptions and confidence
3. Compare options on the same criteria
4. End with named owners and dates

# Required output sections
1. Decision needed
2. Recommendation
3. Options
4. Evidence
5. Risks
6. Next actions

# 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.

Artefacts

1 artefacts

Example Output: Public Policy Executive Decision Memo

Inputs used

Project context: a city service chatbot pilot for multilingual resident support
Target audience: policy teams, public sector product leads, civic technologists
Success metric: activation, quality, and risk reduction
Available tools and data: policy library, service dashboard, public comment tracker, translation QA
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

one-page recommendation, options table, risks, and next actions

Decision needed

The immediate decision is whether a city service chatbot pilot for multilingual resident support is mature enough for a controlled pilot. The strongest evidence should come from policy text and constituent feedback; if either source is missing, mark the recommendation as provisional rather than filling the gap with assumptions.

Recommendation

Recommendation: run a narrow pilot before broad rollout. Prefer a governance-forward pilot if evidence suggests opaque decisions; prefer a speed-forward pilot only when constituent feedback and service metrics are already reliable.

Options

Option A optimizes speed by shipping a limited workflow around service metrics. Option B optimizes control by adding reviewer sign-off and rollback steps. Option C waits until evidence from procurement constraints is stronger. Use the same success metric for all three options.

Evidence

Evidence to trust: procurement constraints, policy text, and reviewer notes from translation QA. Evidence to treat cautiously: anecdotes that are not tied to a time window, cohort, or source owner.

Risks

Treat unequal access as a launch blocker until there is a control that can be verified. The minimum control is: public accountability, plus reviewer sign-off for ambiguous outputs.

Next actions

Next actions: validate constituent feedback, assign a reviewer for opaque decisions, and schedule a decision checkpoint after the first pilot cohort. Do not expand scope until the review path works in practice.

Recommended Decision

Proceed with a narrow pilot focused on policy text and constituent feedback. Treat unequal access as the primary launch blocker. The first milestone should prove that the workflow produces a usable policy brief, pilot plan, and accountability checklist with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.
It includes the required sections: Decision needed, Recommendation, Options, Evidence, Risks, Next actions.
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: unequal access.

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

Public Policy: Executive Decision Memo

Use this prompt when you need one-page recommendation, options table, risks, and next actions for AI-assisted policy analysis, public service workflows, community engagement, and accountability plans.

Best for

policy teams, public sector product leads, civic technologists
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.