Fact-check AI-generated content before it moves forward.
Use FactSentinel as a first-pass review layer for AI-written drafts, summaries, research notes, and source-heavy answers. Keep exact claims, sources, citations, caveats, confidence, and model disagreement visible for human review.
AI-generated content needs claim-level review.
A generated paragraph can sound fluent while mixing supported claims, weak source trails, and invented context. The practical workflow is to isolate claims that carry evidence burden, then inspect the support before publishing, teaching, filing, or sharing.
Source names and citations
Check titles, journals, institutions, authors, links, publication dates, and whether the cited source actually supports the generated claim.
Numbers, dates, and quotes
Prioritize statistics, timelines, direct quotations, named people, policy details, legal context, medical context, and claims that readers may repeat.
Confident uncertainty
Watch for generated answers that sound certain while the evidence is thin, mixed, stale, or absent. FactSentinel keeps caveats and disagreement visible.
Turn generated text into reviewable claims.
FactSentinel does not replace editorial judgment. It helps create a repeatable first pass so the human reviewer can see what needs verification, what looks supported, and where the source trail breaks.
- Extract the claim. Highlight the sentence, citation, statistic, source name, or quote that needs evidence.
- Run a first pass. Use the web checker for pasted text or the Chrome extension when the content is already in a browser page.
- Inspect the evidence. Review sources, reasoning, caveats, confidence, and model agreement before deciding whether to use the content.
- Escalate the risky parts. Send high-stakes, ambiguous, or unsupported claims through manual verification before the content moves forward.
Use it for drafts, summaries, article outlines, and research notes.
The strongest use case is not declaring an entire AI answer safe. It is triaging the claims that deserve inspection so editors, researchers, educators, journalists, and careful readers can move faster without hiding uncertainty.
Checking a single generated claim?
Use the claim checker when the issue is one sentence, number, quote, or named-source claim that needs a visible source trail.
Checking an AI-assisted article?
Use the article verification page when generated content has been turned into a draft article, brief, or source-heavy narrative.
Need broader misinformation review?
Use the misinformation checker when generated text is part of a larger claim trail with article summaries, captions, screenshots, or source snippets.
Need a repeatable team workflow?
Use the workflow guide when the same review process needs to work across drafts, webpages, source checks, citations, and classroom or newsroom review.
Common questions
Can AI-generated content be fact-checked automatically?
FactSentinel can run a first-pass review on exact claims, sources, citations, caveats, confidence, and model disagreement. A human reviewer still decides whether the evidence is sufficient.
What should I paste into the checker?
Start with the generated sentence, paragraph, citation, statistic, quote, or named-source claim that will be published, taught, filed, or shared. Shorter claims usually produce a clearer review trail.
Does this detect whether text was written by AI?
No. This workflow checks whether the claims and source trails in AI-assisted text can be inspected. It is about evidence review, not authorship detection.
When should I escalate to manual review?
Escalate legal, medical, financial, policy, academic, or reputation-impacting claims, especially when FactSentinel shows caveats, model disagreement, missing sources, or weak source support.
Check the evidence before AI text becomes trusted text.
Run a first-pass review on generated claims, citations, and source trails, then keep the human reviewer in control of the final decision.