My co-founder sent me an article last month. It described how Multify works by injecting a JavaScript snippet into the page.
I stared at that sentence for a moment. I wrote the integration layer. We connect via DNS. There are no script injections. That’s how some of our competitors work, not us.
The article had been sitting in a draft for a week. It was grammatically fine. The structure made sense. It had a clear headline and a reasonable conclusion. Nobody who read it would have flagged anything unless they’d built the thing.
It went live before I read it.
This is the actual problem with AI-drafted content. Not that it’s bad, but that it’s confidently wrong in ways that are invisible unless you know what right looks like. The errors aren’t typos. They’re the kind of claim that sounds authoritative and is just subtly off. A competitor’s integration method described as yours. A number approximated where the actual figure exists. A feature described as general availability when it’s still in beta.
The person who wrote the draft doesn’t know. The AI that helped doesn’t know. Only the person who built the thing knows, and that person usually isn’t in the loop until after publish.
Try it on a real AI draft
This is the kind of article that goes live before the founder reads it.
So the question isn’t really how to fix AI-generated content. The question is how to get the right person reviewing it before it goes live, without that review becoming a whole project.
The review needs to happen on a phone, in five minutes, during a gap between other things.
What I’ve found is that the fastest way is to read and speak simultaneously. Scroll through the article. When something is wrong, say it out loud. Not a full rewrite, but just the correction.
“Replace ‘JavaScript snippet’ with ‘DNS-level integration’ (we don’t inject anything.)”
“Change ‘advanced AI-powered translation’ to something specific (this tells the reader nothing.)”
“Soften the claim in the third paragraph (we support most CMS platforms, not all.)”
You’re not editing, but rather simply noticing and narrating. The edit lands in the document. You keep reading.
What to actually look for
The errors in AI-drafted articles tend to cluster in the same places.
Technical implementation details. AI doesn’t know how your product works. It knows how products in your category tend to work. If you made a non-standard architectural decision (an unusual integration method, a different pricing model, a feature built differently from everyone else), it will probably be described wrong.
Numbers and specifics. “Advanced,” “seamless,” “significantly”: these are filler. If you know the actual number, it should be in there. If you don’t, the claim should be softened, not left vague.
Claims about capabilities you haven’t shipped. AI generates confident present tense. If the feature is on the roadmap, not in the product, the article doesn’t know that.
Competitor details written as if they’re yours. This one is the most dangerous. Competitor workflows are well-documented. AI absorbs them. When describing your category, it sometimes reaches for the most common description, which is your competitor’s, not yours.
The review itself isn’t hard. A thousand-word article takes about six minutes. Most of it is reading. The corrections are usually three to five specific things.
The bottleneck was never the review. It was getting me to actually do it before publish.
Checklist: what to review before publishing
Work through this list in order. Most articles clear all ten in under ten minutes.
- Read the technical claims against what you actually built. If your product uses a specific method, protocol, or architecture, check that it’s described correctly, not the category average.
- Replace every vague superlative with a number or remove it. “Significant improvement,” “fast setup,” “most customers”: these need real figures or they need to go.
- Check every feature claim against what’s currently live. Anything on the roadmap should be framed as coming soon, not present tense.
- Search for competitor names and workflows. If a competitor’s approach is in there presented as yours, fix it. If a competitor is mentioned by name, make sure the context is correct.
- Verify every statistic and citation. AI confidently invents research findings. If there’s a “study shows” or a percentage, check that the source exists and says what the article claims.
- Read the opening paragraph and confirm it matches your actual positioning. AI often generates a generic category description as the lede. If you’ve made a deliberate positioning choice, it probably isn’t in there.
- Check that pricing, plans, and availability are correct. Free trial length, plan names, feature tiers. AI doesn’t know about the change.
- Look for passive hedges that undermine concrete claims. “Can help,” “may improve,” “in some cases”: if you know the outcome, state it directly.
- Read the conclusion and confirm it matches what the article actually argued. AI often drifts in the conclusion toward generic advice that doesn’t follow from the specific setup.
- Read the title and confirm it’s specific enough to be useful. Titles like “How AI Can Improve Your Content” describe a category. If the article is about a specific technique or use case, the title should say so.
Mistakes that survive the review
The corrections above are findable if you know what to look for. These ones aren’t. They look right until they aren’t.
The accurate-but-outdated number. An article about your pricing says “plans start at $29/month.” That was true when the AI trained. You changed pricing six months ago. The article passed every factual check at the time of writing and is silently wrong by publish date. The fix: treat any number as a live variable that needs to be verified against the current source, not just the memory of what it used to be.
The correct claim in the wrong context. Your product supports SSO. The article says it supports SSO. But it says it in the paragraph about the starter plan, and SSO is actually an enterprise-only feature. The statement is true. The placement makes it misleading. This is the kind of error that generates support tickets, not corrections. Customers feel misled even though no individual sentence was technically wrong.
The reasonable inference that isn’t yours. AI knows that SaaS products in your category typically have a mobile app. You don’t have one. The article doesn’t say you have a mobile app. It says something like “access your account from anywhere,” which is technically true for a web app but which a reader will interpret as mobile availability. The inference is wrong, the text is defensible, and neither the writer nor a casual editor will catch it.
The deleted feature that stayed in the article. You removed a feature in Q3. The AI draft mentions it because it appeared in earlier content the model was trained on. It passed through two rounds of editing without anyone noticing because both editors assumed the other had verified it. The fix isn’t better editing. It’s having whoever built the feature in the loop before publish.
Frequently asked questions
How long does it take to review an AI blog post?
A 1,000-word article takes about six minutes to review if you’re reading for factual accuracy rather than rewriting. The ten-item checklist above covers the most common error types. Most articles have three to five corrections. The bottleneck is usually getting the right person (the one who built the thing) to actually do the review before publish, not the review itself.
Do I need to fact-check every sentence in an AI-written article?
No, but you do need to check every factual claim, which is a smaller set. The sentences that carry claims are: product descriptions, feature availability, pricing, statistics, named integrations, and comparisons to competitors. Transition sentences, structural framing, and generic explanations rarely introduce errors. Focus on the specific claims and you can move quickly.
What’s the difference between editing and reviewing an AI-written post?
Editing improves the writing: tightening sentences, adjusting tone, improving flow. Reviewing checks that what the article says is true. Both are necessary, but they require different readers. A copy editor can improve the prose without knowing whether the technical claims are correct. Only someone with direct product knowledge can verify the facts. The mistake most teams make is treating review as a subset of editing, and handing the whole thing to whoever is good at writing.