Hallucination review checklist
Large language models produce fluent, confident text — including confident fabrications. The riskiest hallucinations hide inside specific-sounding facts: a precise statistic, an exact date, a named person or company, or a URL that does not exist. This tool scans any LLM output and extracts those claims into a checklist so you can verify each one against a real source before you publish, send, or act on the text.
How it works
The tool splits your text into sentences and runs local pattern matching for the claim types that most commonly turn out to be hallucinated: numbers and percentages, four-digit years and date phrases, capitalised named entities, URLs, and citation-style references. Each matching sentence becomes a checklist item tagged with what triggered it, so you know whether to verify a figure, a name, or a link. You then tick items off as you confirm them, and the running counter shows how much of the output still needs checking.
Tips and notes
- Start with numbers and links. Fabricated statistics and dead URLs are the fastest hallucinations to catch and the most damaging to miss.
- Trace to a primary source. Verifying one model’s claim with another model is not verification — confirm against documentation, an official site, or a peer-reviewed source.
- Re-run after edits. If you ask the model to revise, paste the new version and rescan; revisions frequently introduce fresh fabricated details.
- Nothing leaves your browser. The scan is entirely local, so it is safe to paste sensitive drafts.