AI-generated content is text produced by a language model that predicts likely word sequences rather than reporting something a person observed, and you can spot it before publishing by looking for four concrete signals: sentences of nearly identical length, hedge phrases like "it is important to note," generic examples with no real inputs, and confident claims that have no traceable source.
The fastest check is to read the piece out loud — if every sentence lands at roughly the same rhythm and nothing names a specific number, date, or tool, treat it as unverified until you can trace each claim yourself.
Why those signals show up is worth understanding, because it changes how you scan. A language model generates one token at a time based on what usually follows the previous token, so it gravitates toward the most statistically common phrasing in its training data. That pulls writing toward the middle: medium-length sentences, safe transitions, and conclusions that restate the introduction.
A human writer who actually did something — ran a test, read a report, talked to a customer — breaks that pattern because real details are lumpy. They include a specific number that doesn't fit neatly, a caveat that complicates the point, or a sentence that's oddly short because the thought was finished.
The giveaway isn't that AI text is wrong; it's that it's smooth in a way that lived experience rarely is. According to our AI tool database, which tracks 360 AI tools with pricing and capability snapshots recorded at verification time, the most recent verification date being 2026-09-18, even tool-specific content needs a human to confirm the details haven't shifted since the snapshot was taken.
Here's a short annotated example so the signals are concrete rather than abstract. Take this passage: "In today's fast-paced digital landscape, choosing the right AI writing tool is crucial. It is important to note that many tools offer free tiers, which can be beneficial for beginners.
Furthermore, users should consider their budget and needs before committing." Now mark it up. "In today's fast-paced digital landscape" is a stock opener with no specific subject.
"It is important to note" is a hedge that adds nothing — if it mattered, you'd just state it. "Many tools offer free tiers" names no tool and no price, so it's unfalsifiable. "Beneficial for beginners" is a claim with no mechanism.
And all three sentences run 12-15 words, which is the uniform-length tell. A human version of that paragraph would name a tool, say what its free tier actually includes, and admit where it falls short.
Where this detection approach fails is important to say plainly. None of these signals prove AI wrote something — plenty of human writers use stock phrases and write in even rhythms, especially non-native English speakers or anyone drafting quickly. The signals are probabilistic, not diagnostic.
They also miss heavily edited AI text, where a person has already stripped the hedges and inserted real details; at that point the output is genuinely collaborative and the question of "who wrote it" matters less than whether the facts hold. The practical cost of over-applying this is wasted time rewriting prose that was fine.
So use the checklist as a triage tool, not a verdict: flag anything with three or more signals, then verify the specific claims — names, numbers, dates — against a primary source before you publish. That verification step is the part no detection heuristic can replace, and it's where most publishing mistakes actually get caught.
If you want a deeper walkthrough of the specific tells and how to check them, the guide on telling whether AI wrote a piece of content before you publish it breaks the signals down further.