How to Fact Check AI Content with a 6-Step Audit

The 6-step audit for AI-written articles with numbers

1. Fractionate the draft. Copy every numerical claim into an audit sheet before editing the prose. Include percentages, prices, dates, dimensions, sample sizes, rankings, performance claims, and quoted specifications. Pass: each number has its own row and enough surrounding text to know what it claims. Fail: a sentence contains a number that has not been isolated. Do not edit around it. Extract it first.

2. Separate stated figures from implied math. Mark each row as either a source figure or a derived figure. “Revenue rose 18%” is a source figure. “That equals $42,000 per month” is derived if the draft calculated it from two other numbers. Pass: the sheet shows which figures must be matched and which must be recalculated. Fail: the draft blends a source claim and a calculation in one sentence. Split the claim or remove the calculation.

3. Trace each source figure to the original context. Use lateral reading: leave the draft, open fresh sources, and find the company filing, standards page, product manual, research paper, pricing page, or primary dataset behind the number. A roundup, scraper page, or AI answer is not the end of the chain. Pass: the row links to the original context and names the exact field, table, page, or quoted passage used. Fail: the best support is another summary. Replace the source or cut the claim.

4. Match the figure exactly, including the unit and scope. A number is not verified because it is “close.” Check whether it applies to annual revenue or quarterly revenue, list price or street price, nominal torque or measured torque, North America or global operations. Pass: number, unit, time period, geography, and object match the original. Fail: any one of those changes. Rewrite the claim to the narrower truth or delete it.

5. Recalculate every derived number. Put the formula in the sheet. If the article says a subscription at $49 per month costs $588 per year, the calculation is 49 × 12 = 588. If tax, discounts, or currency conversion matter and the draft does not show them, the figure is not ready. Pass: another editor can reproduce the result from visible inputs.

6. Make the publish-or-remove decision. Verified numbers stay. Corrected numbers get a note in the audit sheet. Unsupported numbers leave the article, even if the sentence sounds better with them. Pass: every numerical claim is marked verified, corrected, or removed. Fail: any row says “probably,” “needs source,” or “AI said.” That draft is not publishable.

What breaks first: a fabricated stat, a real link, and a claim the page never made

1. Copy the numerical sentence into the audit sheet. Keep the number, unit, population, date range, and source label in separate cells.
2. Open the cited page, not the citation text. A polished title, named author, journal name, or plausible URL path proves nothing.
3. Search the source page for the exact number and its denominator. If the article says “42% of B2B buyers,” the source must identify the same group, not “survey respondents” or “marketers.”
4. Classify the failure. Use three labels: fabricated source, real source with invented details, or real page that does not support the claim.

Worked example:

Copilot, Perplexity, and Gemini can attach live source links to their answers. Detection software will not save them, because it misses AI text and also flags human copy by mistake. Check the cited page, not just the domain: RAG systems can hand you a real URL that never actually says what the draft assigns to it.

The decision line: when you need full quantitative auditing, and when a lighter review is enough

1. Trace every number when the article gives advice a reader might act on: pricing, conversion rates, torque settings, equipment dimensions, safety limits, benchmark results, market size, legal exposure, or SEO performance claims.

2. Trace every number when the topic is specialized. General AI models make more mistakes in technical fields, and a plausible-looking specification can still be fabricated, rounded from nowhere, or attached to the wrong product.

3. Trace every number when the claim could create business risk: copyright, defamation, compliance, procurement, warranty language, or a recommendation that affects spend.

4. Use lighter review for low-stakes editorial numbers: dates in a company timeline, counts already visible in your CMS, or simple arithmetic from your own dataset. Even then, check that the number was copied correctly.

5. Remove, don’t soften, any number that cannot be matched. “About,” “roughly,” and “industry average” do not rescue an invented statistic.

Full auditing is too much for a short opinion piece with no quantitative claims. But once a draft uses a figure to persuade, rank, compare, or instruct, that figure needs a source-level match before publication.

Why detector scores do not answer the editor's question

1. Run a detector to triage the draft, not to approve it.
2. Treat the result as an authorship signal only: it estimates whether text looks machine-written.
3. Audit every numerical claim separately. Match it to an original source or remove it.

The objection is fair. A detector is fast. It looks cheaper than assigning an editor to chase every percentage, benchmark, market size, conversion lift, and product specification. Detection tools can help flag suspicious copy, but they are not evidence, because AI detectors produce both false positives and false negatives. A study on scientific abstracts found the GPT-2 Output Detector beat human judges at identifying generated text.

Start there.

A detector can tell you that a paragraph resembles generated prose. It cannot tell you that a claimed “37% reduction in support tickets” came from the company’s own Zendesk export, a vendor case study, a misread chart, or nowhere at all. That is the editor’s question: not “who wrote this?” but “can we stand behind the number?”

There is another wrinkle. Detection is probabilistic. AI detection software produces false positives and false negatives; human readers do too. In full-length medical articles, human raters performed about as well as computerized detectors. QuillBot also defaults unclear text toward human-written to reduce false positive flags, which is a sensible product choice and a bad publication control if you treat “low AI likelihood” as permission to skip verification.

Use the score to decide review order. A high score can move a draft to the front of the audit queue. A low score does not clear a fabricated statistic. A detector can flag risk, but it cannot clear a statistic for publication, because both software and human readers misclassify text in both directions.

BenchWrite's editorial math: how fractionation cuts review time without lowering the bar

1. Extract every sentence containing a number, percentage, rank, date, price, or measured claim.
2. Send that claim set through a secondary AI audit to flag likely mismatches.
3. Trace each surviving number to the original source.
4. Publish only exact matches; delete the rest.

The check starts by breaking the draft into isolated, searchable factual claims, because AI output has to be verified claim by claim, not judged as a single block of prose. Originality.AI can run an automated fact-checking pass on pasted or uploaded text, but a tool pass is only triage.

Worked example: say the draft has 18 numerical claims. Pull the claim sentence plus one sentence of context on either side, averaging 35 words per claim.

18 × 35 = 630 words to verify.

Full reread: 1,800 words. Fractionated review: 630 words. Time removed from close checking: 1,170 words, or 65%.

An initial automated fact-checking pass is useful, but the draft still has to be split into isolated claims and each number checked against an original source. Same bar. Less waste.

Key Takeaways

1. Extract every numerical claim before editing style: counts, percentages, dates, prices, rankings, dimensions, and measured performance.
2. The common mistake is asking another AI tool whether the draft “looks accurate.” Language models predict plausible continuations; they are not evidence systems.
3. Trace each number to the original publisher of the data, not to a scraper, roundup, or rewritten article.
4. Print the figure only when the claim matches the source exactly, including unit, date range, denominator, and product name.
5. If the source cannot be found, or the number changes the claim’s meaning, cut the sentence. Do not rewrite around a guess.

Frequently Asked Questions

How do I fact check AI content step by step?

For a shortened summary of the full 6-step audit, use these 5 checks for numerical claims only: isolate each number, trace it to the original source, match the exact unit and scope, mark pass or fail, and cut every number that fails; if you are auditing authorship rather than factual accuracy, this workflow does not apply. Do this before line editing, because clean prose can hide a bad statistic. A claim does not pass because it sounds plausible; it passes when the source, figure, denominator, and context all match.

Can I use AI to fact check an AI-written article?

Break the draft into isolated, searchable claims first. A model can help find percentages, dates, prices, rankings, and measurements in a draft, but it cannot serve as the evidence for those numbers. AI-generated text also lacks standard publication identifiers such as explicit authors, titles, funding organizations, or publication names.

What counts as a fabricated statistic in AI content?

A fabricated statistic is any number that cannot be traced to a real, primary source or that changes meaning when checked against that source. The most common failures are wrong date ranges, missing denominators, rounded figures presented as exact, and numbers copied from a secondary article that never cites the original data. If the source cannot support the sentence, remove the sentence.

How accurate are AI fact-checking tools?

Do not treat AI fact-checking tools as final reviewers; AI detectors produce both false positives and false negatives, and chatbots can invent citations or attach claims to links that do not support them. That gap is too large for unattended publication work. The safe workflow is mechanical: use tools to speed extraction, then require a human editor to verify every numerical claim against the original record.

Does Google penalize AI content with statistics?

AI drafts need claim-by-claim verification because the model predicts plausible wording instead of checking factual accuracy, and retrieval tools can still attach real links that do not support the statistic being cited. AI drafts earn trust only when every factual claim, citation, and linked source is checked against the original material. Fabricated numbers weaken those signals because they make the article unverifiable.

What should I do when I cannot find the original source for a number?

Cut the number. Do not replace it with “studies show,” “research suggests,” or a softer estimate unless you have evidence for the new wording. For business and technical articles, an unsupported statistic is not a weak citation problem; it is a publishing risk.