Language models write with confidence. That is their nature: they produce fluent sentences, and fluency feels like truth. A model can state a date, a statistic, a name, or a causal explanation in the same smooth tone whether it is reporting something it has solid grounding for or filling a gap with a plausible invention. These inventions are often called hallucinations, and they are not a rare glitch. They are a predictable behaviour of systems that predict likely words rather than looking facts up. The defence is not to stop using AI text, which is often genuinely useful as a draft, but to treat every AI answer as an unverified draft and to check it claim by claim.
The Hallucination Claim Extractor helps with the first step of that defence: breaking a block of AI text into its individual claims. A claim is a single statement that can be true or false, such as "the bridge opened in 1964" or "the drug reduces symptoms by 40 percent." One paragraph of AI output may contain ten or more claims, tangled up with opinions, recommendations, and filler. Pulling them apart matters because verification works on claims, not on paragraphs. You cannot verify "this whole answer looks right." You can verify each sentence-sized assertion, one at a time, and that is where errors get caught.
What counts as a claim
Not every sentence in an AI answer is a claim. Opinions ("this is the best approach"), advice ("you should start with the introduction"), and definitions of terms you supplied yourself are not checkable in the same way. The checkable claims are the factual assertions: dates, numbers, names, places, causes, and rules. A useful sorting habit is to separate claims into three piles. The first pile is verifiable facts, such as historical dates and published statistics, which can be checked against a source. The second pile is reasoning claims, such as "therefore this policy caused the decline," which need an argument and evidence, not just a lookup. The third pile is invented-looking specifics: precise numbers with no source, quotations nobody can find, and citations to papers or books that may not exist. That third pile deserves the most suspicion, because fabricated citations are one of the most common hallucination patterns. If an AI answer cites a study, check that the study exists before you check what it says.
A worked example
Suppose an AI assistant writes a short paragraph about a fictional-sounding but plausible topic: it claims a city library was founded in 1912, holds 80,000 books, runs a reading programme that raised children's scores by 22 percent, and was designed by an architect named in the answer. The extractor pulls out four claims. The founding date can be checked against the library's own website or a local history source. The book count can be checked against the library's published figures, though you should expect a round number like 80,000 to be approximate. The 22 percent improvement is the highest-risk claim: a precise effect size with no study named should never be repeated without a source, and if the source cannot be found, the claim should be dropped, not softened. The architect's name can be checked against the building's history. In this example, suppose the date and the architect check out, the book count is roughly right, and the 22 percent figure has no traceable source. The honest outcome is to keep three claims with their sources noted and to delete the fourth entirely. Editing an AI draft is not about keeping as much as possible. It is about keeping only what you can stand behind.
That example also shows why you should verify before you polish. It is tempting to rewrite AI text into beautiful prose first and check later, but polishing makes false claims feel more authoritative and makes you less willing to cut them. Extract the claims, check them, cut what fails, and only then polish what remains. The order matters more than people expect.
Sorting claims by risk
Not all claims deserve equal checking time. A claim that changes a decision deserves more scrutiny than a claim that decorates one. Before verifying, ask what happens if the claim is wrong. A wrong founding date in a blog post is embarrassing. A wrong dosage, a wrong legal deadline, or a wrong safety instruction can cause real harm. High-stakes claims should be checked against primary sources, meaning the original publication, the official document, or the organisation itself, not another AI summary and not a page that merely repeats the claim. If a high-stakes claim cannot be traced to a primary source, leave it out. A missing fact is almost always safer than a confident falsehood, and readers respect a writer who says a figure could not be confirmed.
Low-stakes claims still deserve a quick check, but a reliable secondary source is usually enough: an encyclopaedia entry, a reputable news report, or an official FAQ. The point of the exercise is not academic perfection. It is building a habit where no factual sentence reaches your reader without at least one moment of doubt and one check.
Common mistakes
The first mistake is checking the easy claims and skipping the hard ones. Dates and names are quick to verify, so people verify those, feel diligent, and then let the one precise statistic slide because it would take longer to chase down. That statistic is usually the claim most worth checking, because precise numbers are exactly what models invent most fluently.
The second mistake is accepting a citation at face value. A citation that looks complete, with author, year, journal, and page numbers, can still be entirely fabricated. Always search for the cited work itself. If you cannot find it in a library catalogue or the publisher's site, treat the claim as unsourced regardless of how convincing the citation looks.
The third mistake is trusting repetition. If three AI answers all state the same fact, that feels like confirmation, but the three answers may share the same underlying patterns and the same gaps. Repetition across AI outputs is not independent evidence. Independent evidence means a source that did not get its information from the model.
The fourth mistake is partial correction. When a claim turns out to be wrong in its details but right in spirit, writers often adjust the number slightly and keep the sentence. Resist that. If the source says something different from the claim, rewrite the sentence to say what the source actually says, or remove it. A claim that has been nudged toward plausibility without a source is still an unverified claim.
Honest limitations
Claim extraction does not make text true. It makes text checkable, which is a different and more modest achievement. Some claims are vague by design ("many experts believe"), and extraction cannot turn vagueness into verifiability; it can only expose it, which is still useful, because a vague claim can then be rewritten or removed. Extraction also cannot catch errors of omission: if the AI answer leaves out the crucial exception to a rule, no list of its claims will reveal the missing piece. That is why extraction pairs with subject knowledge and with reading at least one trustworthy source on the topic yourself, rather than replacing either.
There is also a limit worth stating plainly: this process is a verification aid, not a guarantee, and it is not a substitute for professional advice on medical, legal, or financial questions. If an AI answer touches those areas, the checked claims still need a qualified human's judgement before anyone acts on them.
Closing
Treat AI output as a draft full of assertions, pull those assertions out, sort them by the harm of being wrong, and check them against sources that did not come from the model. Keep what verifies, cut what does not, and polish only afterwards. To trace a claim back to its sources, try the Claim Source Mapper, and to check whether AI text is written at the right level for your readers, use the Readability Score Analyzer. For the wider discipline of checking AI content, read Fact-Check AI Content.