Check generated claims before they become facts.
FactSentinel helps reviewers inspect AI-assisted claims, citations, source names, and evidence trails before confident wording moves into an article, document, classroom, or research note.
AI hallucinations are often source-trail failures.
The risky output is not always obviously false. It may be a confident paragraph with a plausible citation, a real source used for the wrong claim, or a statistic that has lost its context.
Missing sources
Generated text can name papers, reports, policies, or datasets that cannot be found at the title, author, publisher, or date level.
Unsupported claims
A source can be real and still fail to support the exact wording, number, quote, or conclusion in the AI-assisted answer.
False precision
Specific dates, percentages, journal names, and legal references can make a weak source trail look more settled than it is.
A hallucination check should preserve the exact wording.
Use FactSentinel as a first-pass review layer before generated claims are copied into public or high-trust work.
Start with the claim
- Keep the wording intact
- Include the nearby citation or quote
- Avoid rewriting the claim into something easier to verify
Check the trail
- Look for supporting links
- Compare evidence to the claim
- Flag broken, circular, or irrelevant sources
Escalate uncertainty
- Use caveats as review signals
- Treat model disagreement as a stop sign
- Verify manually before publication
Use it when confident AI text needs receipts.
The workflow is built for editors, educators, researchers, journalists, policy reviewers, and readers who need a visible evidence trail before moving AI-assisted text forward.
When fake citations reached a national AI policy draft.
South Africa withdrew a draft AI policy after fictitious sources appeared in its reference list. The case shows why hallucination review has to inspect source trails, not only prose quality.
Need a citation-specific pass?
Use the citation checker when the review target is a reference list, formal citation, journal name, source title, or policy reference.
Need source context and model disagreement?
Use the source checker and model-disagreement note when the question is whether a claim has enough evidence for a human reviewer to trust it.
Checking a full AI-generated draft?
Use the AI content review page when the issue is not only one hallucinated source but a generated draft, summary, or research note with several claims to triage.
Common questions
What is an AI hallucination checker?
It is a review workflow for checking whether AI-generated claims, citations, source names, and evidence trails can be inspected before anyone relies on them.
Can FactSentinel prove AI text has no hallucinations?
No. FactSentinel is a first-pass review workflow. It shows sources, reasoning, confidence, caveats, and model agreement or disagreement so a human can decide what needs manual verification.
What should I check first?
Start with claims that include citations, statistics, named sources, dates, quotes, medical context, legal context, policy references, or anything that will be shared, edited, taught, filed, or published.
Should I use the web checker or Chrome extension?
Use the web checker for pasted AI text and drafts. Use the Chrome extension when you want to check selected text from a webpage, source, article, or research note.
Check the source trail before confident wording spreads.
Use FactSentinel when AI-assisted text includes claims, citations, or named sources that need visible evidence and human review.