Checking AI Output: 5 Checks Before You Publish

Never publish or use anything AI produces — text, numbers, code, an image description — without checking it. The reason is simple: large language models can hallucinate even while sounding completely confident, present outdated information as current, or write in a tone completely disconnected from your brand’s voice. Most of these mistakes go unnoticed until a reader catches them — a wrong figure in a proposal sent to a client, an outdated price, an inappropriately casual tone on a formal page. The 5 checks below systematically weed out these risks before publishing.

Three Risks in AI Output: Hallucination, Outdated Information, Tone Mismatch

AI models write with confidence; that doesn’t mean what they say is true. The three risks I run into most often in the field:

• Hallucination: the model confidently invents a figure, name, or reference that doesn’t actually exist. Especially dangerous in reports and proposal text.
• Outdated information: since training data is frozen at a certain date, the model can present a price, rule, or product that’s no longer valid as if it’s still current.
• Tone mismatch: the model defaults to a neutral-corporate or overly enthusiastic tone by default; it may not match your brand’s actual voice.

These three risks are the rationale for the 5 checks below — each item targets at least one of them.

5 Checks Before You Publish

1. 1. Factual accuracy. Does every number, name, and date in the text come from a real source? How to check it: mark the source (a GA4 report, an invoice, an official document) and match every number the model produced against that source one to one. For example, cross-checking every cell against the source PDF when transferring data from PDF to Excel with AI is exactly this step. If you’re not sure, cut the sentence from the publish.
2. 2. Currency. Is the information still valid — the price, the rule, the product? How to check it: forget the model’s training cutoff date, look at a live source (a current price list, a regulatory page, a product catalog). AI models tend to describe a past state as “right now.”
3. 3. Tone and brand voice. Does the text match the company’s voice? How to check it: read it side by side with 2-3 published pieces; check whether word choice, sentence length, and formality level hold up. This step is especially critical when having AI write ad copy, because ad copy goes straight to the customer.
4. 4. Commitment claims. Are there hard-to-reverse phrases like guarantees or definite outcomes? How to check it: search for patterns like “we guarantee,” “definitely,” “100%” and remove or soften them. AI models have a tendency to produce overblown promises unintentionally.
5. 5. Legal/ethical risk. Are there copyright, privacy, or discriminatory language issues? How to check it: check whether the example, name, or image used was lifted verbatim from a real source, check whether personal data appears, and screen out stereotyping language.

Which Content Type Is Riskier

You don’t need to apply all 5 checks at the same intensity to every piece of content — scale it to the risk:

• High risk: content with numbers and names — performance reports, proposals, invoice summaries, emails going to a client. Mistakes here directly cause loss of trust or a wrong decision.
• Low risk: general, creative content — a blog idea, a social media draft, a headline suggestion. A mistake here is usually caught and fixed by the reader, without serious consequences.

Simple rule: if the content has a concrete number, name, or promise, run the full check; if it’s a general idea or draft, a quick glance is enough.

How Much Checking Is Enough — An Honest Look

Let me be honest: applying all 5 checks in full for every piece of AI output takes time — 15-20 minutes for a number-heavy report, even 5 minutes for a short social media draft. Doing a full check every single time isn’t sustainable in practice.

My method: adjust the depth to the risk. For high-risk content (anything with numbers, names, or promises), all 5 items get worked through one by one. For low-risk content (a blog idea, a draft social post), only tone and commitment claims get a quick scan — factual accuracy and currency checks are usually unnecessary for this type of content, because there’s no concrete claim to verify.

One exception: if a number or name appears anywhere in the content, item 1 (factual accuracy) never gets skipped, regardless of content type.

An Example From the Field

Last month, in one of the accounts I manage, I noticed the weekly performance summary AI prepared listed the click-through rate as 2.4% — the actual figure in GA4 was 1.7%. The model had mixed the previous month’s data into the current report. If I hadn’t cross-checked the number against the source, a false success story would have gone to the client. These mistakes make it to publish not because the model is “bad” — because the source-verification step got skipped.

You Can Ask Me to Do This

This checklist is part of the broader AI Automation Guide. If you want to see how I’ve built AI into my operation end-to-end, continue from there.

If you want to set up this process for your own team: you can ask me to do this. Work that typically takes 2 hours to 2 weeks, remote, billed hourly. See the AI Output Control service page for details.

Frequently Asked Questions

How long does checking AI output take?

Depends on the content’s risk. A short social media draft can be reviewed in 2-3 minutes; a performance report or proposal with numbers and names can take 15-20 minutes.

Do the newest AI models make fewer mistakes?

They may be better, but there’s no zero-error guarantee. Even the latest models can present outdated information as current, or invent a number. Model choice doesn’t eliminate the need for checking, it can only reduce error frequency.

Should the check be done by the person using AI, or by someone else?

In a small business, usually the same person does it, and that’s fine. For number-heavy, high-risk content (a financial report, a legal document, a formal proposal), a second set of eyes is recommended.

What should I do if I find a mistake in AI output?

First fix the sentence and note the correct source. If the same error pattern repeats (e.g., it keeps giving an old price), the problem isn’t in the model — look at the prompt or the source data you provided, and update it.