Fact-checking AI content means treating every specific claim — names, numbers, dates, quotes, and citations — as unverified until you can trace it to a primary source yourself, and the fastest way to do that is to pull each claim out of the draft and check it against the original document, not against another AI summary.
The core rule is simple: if you cannot point to the exact page, file, or record where a claim comes from, it does not go in the published piece. AI models generate fluent text by predicting likely word sequences, so a sentence can read perfectly while containing a company name that never existed or a statistic that was never measured. Your job is not to judge whether the writing sounds confident. Your job is to verify whether each factual claim survives contact with a source you can open and read.
Start by separating the draft into two piles: claims and everything else. Claims are anything a reader could check — "the company was founded in 2019," "the study found a 40 percent increase," "the policy takes effect in March." Everything else is opinion, framing, transitions, and general explanation.
Copy each claim into a list, one per line, and beside it write where you think it came from. Then go find that source. If the draft cites a study, open the study.
If it names a price, open the vendor's pricing page. If it quotes a person, find the original interview or transcript. This sounds slow, but it is the only method that catches the failure mode that matters most: confident, well-written, completely invented detail.
A useful habit is to check the three highest-risk claim types first — numbers, dates, and proper nouns — because those are where a plausible-sounding error does the most damage and where readers are most likely to notice.
Here is a concrete example of the workflow in practice. Suppose an AI draft contains this sentence: "According to a 2024 Stanford study, remote workers were 13 percent more productive than office workers." You would pull out four checkable claims: the institution (Stanford), the year (2024), the metric (13 percent), and the comparison (remote versus office).
You then search for the actual study. If you find a Stanford paper from a different year, or a paper from a different university, or a paper that measured something else entirely, the sentence fails and must be rewritten or cut. If you cannot find the study at all, you cut the sentence — you do not soften it to "some research suggests."
Softening a fabricated citation is worse than deleting it, because it hides the problem while keeping the false authority. The same test applies to tool comparisons: if a draft claims one tool is cheaper than another, open both pricing pages on the same day and compare the actual tiers, because pricing changes and AI drafts often mix up plan names.
Where this method breaks down is worth being honest about. Fact-checking catches wrong facts; it does not catch wrong reasoning, missing context, or a source that is real but weak. A draft can cite a genuine blog post by an anonymous author and still be unreliable, and no amount of link-checking fixes that.
It also costs time — a thorough pass on a long draft can take as long as writing it did, which is why the method works best when you limit how much unverified material enters the draft in the first place. For internal notes, brainstorming, and first drafts nobody outside your team will read, full verification is overkill.
For anything published under your name, anything a customer might act on, and anything with legal, medical, or financial stakes, it is not optional. One practical shortcut: keep a running source file as you work, pasting the exact URL and the exact sentence you relied on for each claim.
When someone later questions a fact, you already have the answer, and you will notice quickly which claims you never actually sourced. If you want to go deeper on catching fabricated citations specifically, the guide on stopping ChatGPT from making up fake facts walks through that narrower problem.
And if your worry is the opposite one — that a draft reads as machine-written even when the facts are fine — that is a separate check with its own method.