Ethical Prompting

The AI Governance Playbook

One-liner: Design a practical AI governance framework for a team or project — covering when to use AI, how to verify outputs, and what requires human judgment.

🔧 Jump in (Tinkerers start here)

Pick a real team, project, or organization you work with. You're going to design an AI usage framework they could actually adopt.

Step 1 — Map the AI touchpoints. Send this prompt:

I'm designing an AI governance framework for a [team type/project type] that does [describe the work]. Map out all the places where team members might use AI in their workflow. For each touchpoint, classify the risk level:

Present this as a table with: Touchpoint | Description | Risk Level | Why

Step 2 — Design the verification tiers. Based on the risk map, create a tiered verification system:

Based on the risk map above, design a 3-tier verification system:

Tier 1 (Low risk): What's the minimum verification needed? What can proceed without review? Tier 2 (Medium risk): What checks are required? Who reviews? What's the turnaround expectation? Tier 3 (High risk): What's the full review process? Who signs off? What documentation is needed?

For each tier, specify:

Step 3 — Write the team guidelines. Now produce the actual document:

Write a 1-page "AI Usage Guidelines" document for this team. It should be practical, not corporate. Include:

  1. When to use AI — Green light scenarios
  2. When to be careful — Yellow light scenarios with required verification
  3. When NOT to use AI — Red light scenarios or scenarios requiring explicit approval
  4. Verification standards — The tier system from above, simplified
  5. Attribution — When and how to disclose AI usage
  6. Escalation — What to do when you're unsure whether AI use is appropriate

Write it in the tone of a senior colleague giving practical advice, not a legal department issuing mandates.

Step 4 — Red-team the framework. Test it:

Now role-play as a team member who wants to use AI in a gray area. Come up with 3 realistic scenarios where the guidelines are ambiguous or where a reasonable person might interpret them differently. For each scenario, suggest how to clarify the guideline.

Revise the guidelines based on the edge cases.

📋 Plan first (Planners start here)

Here's what you're about to do:

  1. Choose your context — Pick a real team or project. The framework should be one you could actually share or implement.
  2. Map AI touchpoints and risk levels — Identify every place AI could be used in the workflow and classify each by potential harm from errors.
  3. Design tiered verification — Create different verification processes for different risk levels. Not everything needs the same scrutiny.
  4. Write the guidelines — Produce a practical 1-page document that a team member could reference in their daily work.
  5. Red-team with edge cases — Test the framework against ambiguous scenarios. Fix any gaps before sharing.

"Done" looks like: A complete, practical AI governance framework (risk map + tiered verification + 1-page guidelines) that you could present to your team, plus documentation of edge cases you tested against.

🧭 Why this matters (Strategists start here)

Individual verification habits (from EP-Intermediate-01) don't scale to teams. When five people use AI differently with different standards, the team's AI output quality is only as good as the weakest link. A governance framework creates shared standards without bureaucracy — it tells people what's safe to do quickly and what requires care, without making every AI interaction feel like a compliance exercise. This is also the document organizations will pay for: a practical, calibrated AI usage policy that actually gets followed.

Reflection

⬆️ Level up

You've reached the advanced level for Ethical Prompting & Judgment. From here, consider:

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The Fact-Check Habit

One-liner: Catch an AI making a confident mistake — and build a simple verification process you'll use every time.

🔧 Jump in (Tinkerers start here)

Pick a topic you know well — your industry, your hobby, your area of expertise. Something where you can spot errors.

Step 1 — Get a confident answer. Send this prompt:

Give me a detailed overview of [topic you know well]. Include specific facts, statistics, and examples. Be thorough and authoritative.

Read the output carefully. Find at least one claim that feels off. It might be a statistic that seems too round, a date that feels wrong, a name that's slightly off, or a causal claim that oversimplifies reality.

Step 2 — Make the AI check itself. Send this:

Look at your previous response. I want you to fact-check yourself. For each specific claim, statistic, or example you cited:

  1. Rate your confidence (high / medium / low)
  2. Flag anything you might have fabricated or estimated
  3. Identify which claims are most likely to be wrong and why

Be ruthlessly honest. I'd rather know what you're uncertain about than have you defend everything.

Step 3 — Verify. Pick the 2-3 claims the AI flagged as lowest confidence. Google them. Were they accurate, close but wrong, or completely fabricated?

Step 4 — Build your check. Based on what you just learned, write a 3-line "verification prompt" you can append to any AI output:

Before I use this, tell me:

  1. Which specific claims are you least confident about?
  2. What did you estimate or approximate vs. know with certainty?
  3. What should I verify independently before sharing this?

Save this somewhere you'll see it. Use it as a default follow-up to any AI output you plan to rely on.

📋 Plan first (Planners start here)

Here's what you're about to do:

  1. Choose a familiar topic — You need to be able to spot errors, so pick something in your area of knowledge. Don't use an unfamiliar topic — you won't know what to verify.
  2. Generate an authoritative-sounding response — Ask AI for a detailed, factual overview. The more specific and confident the output, the more likely it contains subtle errors.
  3. Ask AI to fact-check itself — Use the self-audit prompt to force the AI to rate its own confidence and flag potential fabrications.
  4. Independently verify — Pick the lowest-confidence claims and check them against reliable sources. Track what was right, close, and wrong.
  5. Create your verification template — Build a reusable 3-question follow-up that you'll use after any AI output you plan to act on.

"Done" looks like: You've caught at least one AI error, you understand why the AI got it wrong, and you have a saved verification prompt you can use going forward.

🧭 Why this matters (Strategists start here)

The community's Ethical Prompting score is 75% — the highest of all five pillars. Most people know they should verify AI output, but few have a systematic process for doing so. This exercise closes the gap between awareness and practice by giving you a concrete, reusable tool. The verification prompt you build here becomes a habit — a 30-second step that catches errors before they become problems. At the intermediate level, you'll build a more comprehensive verification checklist; this exercise establishes the baseline behavior.

Reflection

⬆️ Level up

Ready for more? Try EP-Intermediate-01 — where you'll build a comprehensive verification checklist and stress-test it against real AI outputs.

Back to Ethical Prompting & Judgment

The Verification Checklist

One-liner: Build a personal AI verification system — a checklist you'll actually use — and stress-test it against real AI outputs to find its limits.

🔧 Jump in (Tinkerers start here)

You're going to build a verification checklist, then immediately try to break it.

Step 1 — Generate something to verify. Ask AI to produce a piece of content you might actually use in your work:

Write a [deliverable type — e.g., client email, project proposal, market analysis, technical recommendation] about [topic relevant to your work]. Make it detailed and specific. Include data points, recommendations, and reasoning.

Step 2 — Build your checklist. Before reading the output carefully, write your own verification checklist. Start with these categories and add your own:

Check Question Pass/Fail
Factual claims Are specific numbers, dates, or statistics verifiable?
Sources Could I find the original source for any cited information?
Reasoning Does the logic hold? Are there hidden assumptions?
Completeness What important perspective or consideration is missing?
Tone/audience Is the tone appropriate? Would the intended audience trust this?
Actionability Are the recommendations specific enough to actually follow?
Your domain check [Add a check specific to your field]
Your domain check [Add another check specific to your field]

Step 3 — Apply the checklist. Go through the AI output line by line using your checklist. Mark each check as pass or fail. For every fail, note what the issue is.

Step 4 — Stress-test the checklist. Now deliberately ask AI to produce something harder to verify:

Write the same type of [deliverable] but on a topic I'm less familiar with: [topic outside your expertise]. Make it equally detailed and authoritative.

Apply your checklist again. Where does it fail to catch problems? What check do you need to add?

Step 5 — Finalize. Update your checklist based on what you learned. Save it where you'll actually use it — bookmark it, pin it, print it, whatever works.

📋 Plan first (Planners start here)

Here's what you're about to do:

  1. Generate test content — Ask AI to produce a work-relevant deliverable. This gives you realistic material to verify.
  2. Draft your checklist — Build a structured verification list covering factual accuracy, reasoning quality, completeness, tone, and domain-specific concerns.
  3. Apply to familiar territory — Use the checklist on AI output about a topic you know. This lets you calibrate how well the checklist catches real errors.
  4. Apply to unfamiliar territory — Use the checklist on AI output about a topic you don't know well. This exposes gaps in your process — the errors you can only catch with domain knowledge.
  5. Iterate and save — Update the checklist based on what it missed. Save it in a format you'll actually reach for.

"Done" looks like: A tested, refined verification checklist (8-12 items) saved in a usable format, with evidence of at least one error it caught and one gap you identified and fixed.

🧭 Why this matters (Strategists start here)

In EP-Basic-01, you built a simple 3-question verification prompt. Here, you're building a systematic process — a checklist that works regardless of topic, catches both factual and reasoning errors, and is tuned to your specific work context. The community's 75% Ethical Prompting score means most people intend to verify AI output but lack a consistent method. A checklist turns good intentions into reliable behavior. At the advanced level, you'll scale this into a governance framework for a team; this exercise builds the individual practice first.

Reflection

⬆️ Level up

Ready for more? Try EP-Advanced-01 — where you'll design an AI governance framework for a team or project.

Back to Ethical Prompting & Judgment