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 — 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.