An article goes live explaining how a SaaS product connects to a customer’s website. It says the product works by injecting a JavaScript snippet. The product actually connects at the DNS level. Nothing is injected. The entire mechanism is different.
The article was AI-written and reviewed by someone who knew the product well enough to use it but not well enough to catch that particular error. By the time someone who built the product read it, it had been indexed for weeks.
This is the specific failure mode of AI writing about technical products. The model doesn’t fabricate randomly. It reaches for the mechanism that fits the category. Website integrations often use JavaScript snippets. So the AI wrote “JavaScript snippet.” The claim was plausible, common, and wrong.
Why technical errors survive review
The people who review content are usually not the people who built the thing. A marketing manager can catch a typo, a tone problem, a factual claim about pricing. They can’t catch a claim about how the DNS integration works if they’ve never set one up.
The people who could catch it, the engineers and founders, aren’t in the review loop. Not because nobody thought to include them. Because the review loop for content usually doesn’t exist. The article gets written, someone skims it, it gets published.
AI makes this worse because the technical details in AI-generated content read as confident and specific. A made-up number is easy to spot as suspicious. “The integration works via DNS, not a JavaScript snippet” doesn’t read as suspicious. It reads as a detail the writer knew.
Try it on an AI-written technical explainer
An article about how a hypothetical integration product works. Sounds correct. Some of it isn't.
The claims worth checking
How the product works mechanically. This is the highest-risk category. AI will infer the mechanism from the category: SaaS integrations use API keys or OAuth, website tools use JavaScript, payment tools use webhooks. If your product does something different or works at a different layer than the category default, the AI will get this wrong and sound confident doing it.
Numbers. Response times, storage limits, user counts, processing speeds. AI generates plausible numbers. “Processes up to 10,000 events per second.” “Supports teams of any size.” These aren’t fabricated in the sense of being random. They’re fabricated in the sense of being inferred from similar products. If you haven’t supplied the real number, the one in the copy isn’t yours.
What requires what. “Requires admin access.” “Works without a credit card.” “No coding required.” Each of these is a claim about a user experience or a prerequisite. AI will fill these in based on what’s common for the category. If your product genuinely requires no coding but the copy says “requires a developer to configure,” that’s a conversion problem. If the copy says “no coding required” but it actually does, that’s a support problem.
Integration names. AI will list integrations that sound right for a product in your category. Slack, Salesforce, HubSpot, Zapier. If some of those aren’t live, or if you have integrations that aren’t on the standard list, the copy will be wrong in both directions: claiming integrations you don’t have and missing ones you do.
The edge cases in the technical architecture. These are the hardest to catch because they require knowing the product at the implementation level. The AI won’t get the happy path wrong. It gets the details wrong: the order things happen in, which system is authoritative for what data, what happens when a step fails. An article saying “syncs in real time” when the sync actually runs every 15 minutes is one example. Copy saying “changes take effect immediately” when there’s a propagation delay is another. The error looks small until a customer reads it and then opens a support ticket asking why their change didn’t take effect immediately.
Getting the right person to read it
The fix isn’t a better prompt. It’s a different reviewer.
For any content that makes a technical claim, the person who built that part of the product needs to read it. Not skim it. Read it in the specific mode of “is this accurate about how this works.” That’s a different kind of reading than checking for tone or clarity, and it needs to happen before the content is published, not after someone notices an error in a support ticket.
That review is usually five minutes. Finding the error after it’s been indexed and linked is longer. The draft needs to reach the right person while it’s still a draft.
Reading it out loud helps with this. Technical errors that survive a silent skim often surface when you’re speaking the claim: “the integration works via JavaScript injection” sounds different when you say it to someone who knows it doesn’t. The wrongness becomes audible in a way it doesn’t on a screen.
The goal isn’t to stop using AI for technical content. Most of the writing is fine. The problem is that the errors are invisible to everyone except the person who built the thing, and that person usually reads the article after it’s already live.