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: 
 
 Generate test content — Ask AI to produce a work-relevant deliverable. This gives you realistic material to verify. 
 Draft your checklist — Build a structured verification list covering factual accuracy, reasoning quality, completeness, tone, and domain-specific concerns. 
 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. 
 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. 
 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