Before you paste AI output into anything that matters at work, run it through eight checks: task fit, invented facts, missing context, softened risk, wrong owners or dates, tone, what needs verifying, and who signs it off. The point is plain: stop weak output reaching real work with your name on it.
When does this matter?
Run the full check when the output goes to someone else, carries a decision, or has your name on it. A status update, a risk summary, a client email, a board paper. If you are using AI to think out loud or draft something only you will read, you do not need all of this. The check is for the moment before it leaves your hands.
What actually goes wrong with AI output at work?
The trap is fluency. Tools like ChatGPT, Microsoft Copilot and Claude return clean, confident prose whether or not the content underneath is sound, and people read that confidence as accuracy. Their own makers print a line under the box saying they can get things wrong, and they mean it.
The failures follow a pattern. It invents specifics it could not know, a date, an owner, a figure, a citation. It softens problems, because measured language reads better than alarm. It answers a nearby, easier question than the one you asked. None of this looks like a malfunction. It looks like a finished draft, which is exactly why it slips through.
How do you check AI output before using it?
Eight checks. Most take seconds once they are a habit.
You can make the tool do some of this work for you. Paste this after its answer:
It will not catch everything, and it has no reason to flag its own confident inventions reliably, so it speeds up the check rather than replacing it.
- Task fit. Did it answer the question you actually asked, or an easier one nearby? Re-read your request, then the output, and confirm they match.
- Invented facts. Names, figures, dates, quotes, citations, links. The tool fills gaps with plausible inventions. Treat every specific as unverified until you have checked it.
- Missing context. The output only knows what you gave it. Anything it could not know, internal history, a decision made last week, a constraint you forgot to mention, is either guessed or absent.
- Softened or inflated risk. Check whether a real problem got rounded down, a serious blocker written up as a minor delay, or confidence got rounded up. This is the dangerous one for status reports and reviews.
- Wrong owners and commitments. Did it hand an action, a deadline or a decision to someone with no basis? It will invent an owner rather than leave a blank.
- Tone and audience. Right register for who reads it. AI defaults to fluent and faintly salesy; most work writing is plainer than that.
- Verification route. For each claim that carries weight, can you trace it to a source? If you cannot, it does not go out.
- Sign-off. You own the final version, the tool does not. Read the whole thing once, as yourself, before your name goes on it.
Before I use this, list every specific claim, name, figure and date in your
answer that I should verify before sending. For each one, tell me whether you
knew it from what I gave you or inferred it. Be blunt, and flag anything you are
not sure about.What should you never send without checking?
Some output is higher stakes than the rest. Always verify before these leave your hands: any figure or date a decision rests on, anything assigning blame or ownership, citations and links, risk and status wording, and anything touching legal, financial, HR or safety matters. If it is one of those, the eight checks are not optional.
Worked example: an AI-written status update
Say you ask Copilot to turn a project meeting transcript into a weekly status update. It comes back clean and confident. Run the check. Task fit: it summarised the meeting, but did it produce a status update with risks and next steps, or just tidy minutes? Invented facts: it states a go-live date, did anyone actually say that, or did it infer it from the discussion? Softened risk: the meeting flagged a supplier blocker as serious, the summary calls it a minor delay. Owners: it assigned the integration fix to a name nobody mentioned. None of that is the tool behaving strangely. It is the normal failure pattern, which is why the check is a habit and not a one-off.
What if you are short on time?
No time for eight? Do three. Check the names and numbers are real, check that no risk got softened, and read it once yourself before it goes out. That catches most of what would embarrass you. Save the full pass for anything going above your head.
FAQ
Can you trust AI output at work?
You can use it, but you cannot send it unchecked. Treat it as a confident first draft from someone who has never seen your project, then verify the specifics before your name goes on it.
What is the most common AI mistake in work documents?
Invented specifics that read as fact. A date nobody set, an owner nobody named, a figure that sounds right. The fluent tone makes them easy to miss.
How do you stop AI making things up?
You cannot stop it entirely. You can ask it to list which claims it inferred rather than knew, and then verify those yourself. The check is the safeguard, not the prompt.
Is checking AI output worth the time?
One wrong figure in a board paper costs more than the two minutes the check takes. For anything that carries a decision, it pays for itself the first time it catches something.
Related: the AI Tool Test Log for recording what you check and what broke, the guide on how to write AI prompts for real work so the output needs less fixing, and the opinion piece on why most AI demos skip the bit that matters.