Review AI-assisted claims before they become finished copy.
A practical workflow starts with the exact wording, then checks the source trail, citations, model disagreement, caveats, and risk level before a claim moves into an article, classroom, report, or research note.
Separate the claim, the evidence, and the decision.
AI-assisted text can sound finished before the evidence is finished. Treat the first pass as triage: preserve what was claimed, inspect the sources, and decide what needs deeper verification.
1. Preserve the exact claim
Review the specific sentence, quote, citation, statistic, date, or policy reference before rewriting it. A vague paraphrase makes the check less useful.
2. Check the source trail
Look for real source links, matching titles, relevant evidence, broken citations, circular references, and places where confidence is higher than support.
3. Escalate by risk
Manual review matters most when the claim is high-stakes, consequential, legal, medical, policy-related, reputational, or about a person or organization.
Different evidence problems need different entry points.
Use the web checker or extension for a quick first pass, then move to a focused page when the risk is about sources, citations, hallucinations, or tool selection.
Exact claim review
Use this when the first question is whether a specific sentence, quote, statistic, or date has enough source support to move forward.
Article triage
Use this when a full article has several claims and the first pass needs to keep the claim, source support, caveats, and model disagreement visible for human review.
Generated content review
Use this when AI-written drafts, summaries, or research notes need claim-level source review before they move into a human workflow.
AI hallucination review
Use this when a draft, answer, or source list may contain invented facts, overconfident wording, fake references, or unsupported claims.
Source and citation review
Use these when the claim depends on named sources, references, policy documents, statistics, academic citations, or a bibliography that needs receipts.
Prior-art and tool comparison
Use comparison and field-note pages when the question is whether a published check already exists, which workflow fits, or why model disagreement should slow the review down.
Use real failures to decide where the workflow should slow down.
The South Africa draft AI policy fake-sources incident is a useful reminder that polished source lists still need verification. A workflow should make the source trail visible before a claim moves forward.
Fit the review to the work in front of you.
Editors and journalists
- Check article claims before publication.
- Slow down around model disagreement or thin sourcing.
- Keep caveats visible for manual review.
Educators and researchers
- Inspect AI-assisted references before classroom or research use.
- Show students where evidence supports or fails a claim.
- Separate first-pass review from final judgment.
Readers and teams
- Check a post, paragraph, or report before relying on it.
- Use the Chrome extension when the claim is already on a page.
- Use the web checker when the claim is pasted from a draft.
Common workflow questions
What should an AI fact-checking workflow include?
It should preserve the exact claim, check source links and citations, show confidence and caveats, make model disagreement visible, and escalate high-stakes claims to manual verification.
Where does FactSentinel fit?
FactSentinel is a first-pass browser and web workflow. It helps reviewers inspect a claim and its source trail before deciding what needs deeper manual review.
Can this replace human fact-checking?
No. The workflow is meant to expose uncertainty and reduce review friction. It does not replace editors, researchers, educators, legal review, medical review, or subject-matter experts.
Start with the exact claim.
Paste a claim into the web checker or install the Chrome extension when you need to review text in context on a page.