AI fact-check claim extractor
AI writing tools are excellent at sounding authoritative and terrible at being reliably correct. The dangerous parts of an AI draft are its specific factual assertions — a precise statistic, a date, a named source, a quoted line — any of which can be confidently hallucinated. The fact-check claim extractor scans AI-generated text, pulls out every sentence that contains a verifiable claim, and presents them as a checklist so you can confirm each one against a real source before you publish. It runs entirely in your browser.
How it works
The extractor splits your text into sentences and scores each one for fact-check signals: numbers and percentages, calendar dates and years, capitalised named entities (people, organisations, places), text inside quotation marks, and citation or reference markers. Sentences carrying these signals are surfaced as claims, grouped by the type of evidence they contain, so you know what kind of source you need to confirm them. Sentences that are purely opinion, transition, or generic filler are left out, keeping the checklist focused on the statements that actually carry factual risk.
What hallucination actually looks like in AI output
The problem is not that AI gets things obviously wrong — it is that it gets things plausibly wrong. The patterns that appear most often in AI-generated content that needs fact-checking:
Invented statistics: A model may write “studies show a 34% improvement” when no such study exists, or misremember a real figure and reproduce it with the wrong percentage. The number sounds authoritative and specific, which is exactly why it needs checking.
Date and timeline errors: Models can place events in the wrong year, confuse the order of events, or invent founding dates and tenure lengths for named organisations. Dates are among the most commonly hallucinated facts.
Fake citations: AI frequently generates plausible-sounding academic citations — journal names, author names, volume numbers — that refer to papers that do not exist. These are particularly dangerous because they look like evidence.
Misattributed quotes: A quote attributed to a named person may be paraphrased inaccurately, may belong to someone else, or may be entirely fabricated. The quotation marks themselves create a false sense of precision.
Wrong named-entity facts: A company’s founding year, a politician’s position at a given date, a country’s population or GDP — these are frequently garbled.
The extractor pulls every sentence that could contain any of these patterns, so you can verify the risky parts efficiently without reading every word.
A practical workflow for editorial teams
- Generate the AI draft as usual
- Paste it into the extractor — the checklist appears in seconds
- Work from highest-risk category down: citations first, then statistics, then named-entity facts, then dates
- For each claim, open the primary source (the actual paper, the official statistic, the original quote source) — not another AI summary
- Tick off confirmed claims; rewrite or remove anything that cannot be verified
- Keep the checklist in your editorial record as evidence of due diligence
Tips and notes
- Prioritise numbers and citations. Invented statistics and fake references are the most common and most damaging hallucinations — check those first.
- Confirm against primary sources. Verify a date or quote at its origin, not against another AI summary that may share the same error.
- It is a first pass. Subtle claims phrased without numbers or names can slip through; read the full text too.
- Keep the checklist. Attaching the extracted claims to an editorial review gives you a defensible record of what was verified.
- Re-run after editing. If you ask an AI to rewrite a section based on verified facts, extract and check the new text too — models can re-introduce errors when paraphrasing.