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:

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

• Which check caught the most problems? Which was least useful?
• How did your verification experience change between the familiar topic and the unfamiliar one?
• Is your checklist something you'd actually pull up before sending an AI-generated deliverable? What format makes it most likely you'll use it?
• 💬 Trade checklists with a colleague. Have them apply yours to an AI output from their work — their feedback will reveal blind spots specific to your domain. (Social Learners)

⬆️ 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