How to Fact-Check AI-Assisted Content

Fact Checking AI Content: A Practical Verification Pass for Editors

By Sarah Kessler, editorial standards lead. Ten years fact-checking for newsroom and agency clients, now focused on AI-assisted publishing workflows.

A generated draft can state something false in exactly the same tone it uses for the truth. There's no wobble in the prose when a model gets it wrong, no tell that separates a fabricated statistic from a verified one. That's the whole problem with fact checking AI content: you can't scan for it, you have to check for it.

This matters because line editing and fact-checking are different jobs, even though they often get treated as one pass. A line edit fixes rhythm, cuts flab, sharpens a clumsy sentence. None of that touches whether the claim inside the sentence is true. You can polish a paragraph until it sings and still publish something wrong, because fluency and accuracy are produced by two different kinds of attention. One is about how it reads. The other is about whether it happened.

Why fluent copy still needs verification

Fluent copy needs verification because a language model is built to predict plausible text, not to confirm true text. It generates the most statistically likely next words given what came before, and most of the time that lines up with reality, since the training data is mostly accurate. But when the model doesn't actually have a fact, it doesn't pause or flag uncertainty. It produces something that fits the shape of a correct answer.

OpenAI's own documentation says as much: ChatGPT can produce fabricated quotes, citations, and studies, and users are told to verify important information against reliable sources rather than trust the output outright (OpenAI). That's not a bug report about one bad session, it's a structural warning about how these systems work. Google said something similar after a Gemini image-generation controversy in February 2024, calling hallucination a known challenge across large language models and pointing users back to search for anything current or high-stakes (Google).

A useful reminder in the same territory: you cannot ask a model whether it wrote something, or whether a claim inside its own output is accurate, and expect a reliable answer. OpenAI is explicit that ChatGPT has no record of whether it generated a given piece of text (OpenAI). Self-certification isn't a shortcut here. The verification has to happen outside the tool that produced the draft.

In practice this shows up as a small set of recurring failures. The model invents a specific figure that sounds exactly right and has no basis anywhere. It manufactures a citation with a plausible journal name and a correctly formatted link, pointing to a source that either doesn't exist or doesn't say what's claimed. It states a fact that was true as of its training cutoff and is now stale, a price, a version number, a person's job title. Or it reasons its way to a wrong conclusion, reversing cause and effect or misdescribing a mechanism, with nothing as obvious as a fake citation to catch it on. A newsroom example from the Columbia Journalism Review's 2023 review of AI search tools found several assistants citing real outlets for quotes those outlets never published, which is the citation-fabrication problem showing up outside a lab setting. Each of these reads as confident because confidence is structural to how the text gets generated. It has nothing to do with whether the underlying claim is real.

A practical fact-checking pass

Fact checking AI content works best as a deliberate pass with a fixed order, not a read-through where you fix things as you notice them. Break it into four steps and keep them separate:

• Flag. Read once and mark every specific, checkable claim: statistics, dates, names, quotes, citations, superlatives, anything time-sensitive. Don't verify yet.
• Rank. Sort flagged claims by how much weight they carry. A stat anchoring your central argument outranks an aside in paragraph six.
• Verify. Check each claim against an independent, primary source, and confirm the source says what you think it says, not just that it exists.
• Cut or caveat. Correct what you can correct. Cut what you can't verify. Caveat what's genuinely uncertain but worth keeping, with honest attribution attached.

If a sentence is too vague to check at the flag stage, that's useful information on its own. Sharpen it into something specific or cut it there and then.

Here's the pass applied to one paragraph, start to finish. The draft reads: "A recent industry report found 68% of marketers now use AI daily, and adoption is only accelerating." Flagging catches two things: the specific percentage, and the unsourced "recent industry report" it's pinned to. Ranking puts it near the top, since the paragraph opens the piece and everything after leans on it. Verifying means searching for the actual report rather than a blog post that cites the figure secondhand. Say that search turns up a real survey, but one from a different year with a different sample size and a figure closer to 54%. That's enough to disqualify the original sentence. The revised paragraph reads: "A 2023 survey by a marketing software vendor found 54% of respondents used AI tools at least weekly, though the sample skewed toward larger firms already invested in the category. Adoption is trending upward, but daily use industry-wide isn't yet established by the available data." Nothing punchy got restored. What's left is smaller and true, which is the trade the whole pass is built around.

At an agency or newsroom, these four steps map onto roles rather than staying with one person. A junior editor or assistant often does the flag pass, since it's mechanical and doesn't require institutional judgment. Ranking gets done, or at least reviewed, by whoever owns the piece editorially, because it requires knowing what the argument actually depends on. Verification sometimes gets split across a research desk and the writer, particularly on longer features where claims span several beats. The cut-or-caveat decision, though, usually needs a senior sign-off, especially on anything going out under a masthead, because that's the point where reputational risk gets decided one way or the other.

Two AI outputs agreeing with each other isn't corroboration. They can share the same wrong training pattern and produce the same wrong answer twice, which is why the verify step has to involve a source outside the model that wrote the draft. That can mean a search engine, a library database, a primary document, or in some newsroom workflows a dedicated fact-checking tool like Full Fact's automated claim-matching system, which cross-references statements against a database of previously checked claims, though those tools still need a human confirming the match is genuine and not a coincidental keyword overlap. Whatever the method, the check happens outside the generation loop, never inside it.

The last step is where discipline actually gets tested. The instinct is to keep an unverified claim because deleting it feels like losing content. An unverifiable claim adds risk without adding anything back, so it should go. The editing checklist for AI drafts puts accuracy as its first section for exactly this reason, before tone, before structure, before anything else gets touched.

It's worth keeping a short record of what got cut or caveated and why, especially on anything published under a byline that carries weight. A line noting "stat removed, could not locate source report" takes ten seconds to add and saves a repeat argument the next time someone asks why a number disappeared between drafts.

What deserves the most scrutiny

Verification effort should track risk, and some claim types are both easy to get wrong and expensive when they are.

| Claim type | Why it's risky | Verification priority | |, |, |, | | Statistics and percentages | Specificity reads as credibility, even when invented | Highest, trace to original dataset | | Fabricated citations | Format is easy to fake, substance often isn't real | Highest, check the source exists and says what's claimed | | Quotes | Models misattribute or paraphrase as verbatim | High, verify exact wording and speaker | | Dates and superlatives | Plausible-sounding errors, rarely literally true | Medium to high depending on context | | Time-sensitive facts | Correct at training cutoff, stale by publication | Medium, re-confirm as true today | | Medical, legal, financial claims | Wrong ones cause harm, not just embarrassment | Highest, cut if unclear rather than soften |

Statistics and percentages top the list, because an invented number reads as more credible than the vague truth it replaces. A made-up "73% of teams report..." looks authoritative precisely because it's specific. Trace every one to the original dataset or study, not to a secondary summary of it.

Fabricated citations sit close behind. A citation's format is easy to learn and easy to fake, a plausible author, a correctly styled DOI, a journal name that sounds right, so the model nails the shape and invents the substance underneath it. Quotes carry the same risk in a different form: models misattribute lines or paraphrase something and present it as verbatim. Verify the exact wording and the actual speaker before it goes anywhere near publication.

Dates and superlatives deserve more care than they usually get. It's easy for a model to be a year off in a way that sounds perfectly plausible, and words like "first," "largest," or "only" are rarely true exactly as stated, they need either hard evidence or a softer, defensible phrasing. Time-sensitive facts, prices, version numbers, current officeholders, go stale silently once the world moves past a training cutoff, so re-confirm anything of that kind as true today, not as true whenever the source was written. Medical, legal, and financial claims get the highest bar of all, because a wrong one causes real harm rather than just embarrassment, and an unclear one should be cut rather than softened.

Primary sources matter more here than secondary ones. A primary source is the origin, the actual study, the official filing, the law itself. A secondary source reports on the primary one, a news article summarizing a study, a blog post citing a report, and every layer between you and the origin is a chance for distortion. When a news article says "the study found X," go read the study. Often it had caveats the summary dropped, or the percentage got rounded into something the data doesn't quite support. Watch for circular verification too, where several sources all trace back to the same unchecked original, which might itself have been an AI-generated error picked up and repeated. Beyond dedicated claim-matching tools, plain search-engine verification and library databases like JSTOR or PubMed remain the workhorses for anything academic, and neither replaces the step of reading the primary document itself. The Associated Press applies a version of this same caution to any sourced claim, checking for the original source and looking for independent reporting from trusted outlets before trusting a fact (AP). NIST's generative AI risk profile, released in July 2024, frames this kind of verification as part of a broader governance process rather than a one-off editorial task, which is closer to how it should function inside a real publishing workflow (NIST).

There's a reason to take this seriously beyond avoiding embarrassment. Reuters Institute's 2024 Digital News Report found that some US respondents already expect generative AI to make misinformation harder to detect, particularly around politics and elections (Reuters Institute). Content that skips verification isn't just risking one wrong article. It's adding to a pattern readers are already primed to distrust. And for anyone optimizing toward answer engines, verified specificity is also what makes content worth citing in the first place, a point covered in more depth in generative engine optimization.