Best AI for Fact-Checkers

Fact-checking is one of the most misunderstood skills in journalism and publishing. People who haven't done it assume it's simple: check if the thing is true. In practice, it involves deciding what claims need checking, finding the right primary sources for each type of claim, understanding what level of confirmation is adequate for a given publication's standards, and documenting the source trail in a way that stands up to questions. None of that is straightforward, and AI doesn't simplify the judgment calls at the center of it.

What AI does well for fact-checkers is the retrieval and research work: finding sources quickly, organizing claims for systematic checking, and building a citation record efficiently. Done correctly, AI assistance makes fact-checking faster without compromising standards. Done incorrectly, AI introduces a new class of errors that are particularly dangerous because they look like confirmed facts.

This guide covers three tools that support the source-finding and research side of fact-checking, with specific attention to what each tool can and can't be trusted to do.

The fact-checker's workflow and where AI fits

Professional fact-checking on a long-form article or book typically follows a systematic process. The fact-checker goes through the manuscript claim by claim, identifies what needs verification and what type of verification is appropriate, finds primary sources, and builds a record that documents what was checked and what supports each claim.

The time-intensive parts of this process are:

Claim identification and triage: What in this piece is a factual claim that needs checking? Not every assertion needs primary source verification. Experienced fact-checkers triage quickly. AI can help identify discrete checkable claims in a long text.

Source finding: For a given claim, what's the right primary source? Who originally published this data? What government database has this record? AI tools that search and synthesize quickly help find the right source to verify against.

Citation building: For each verified claim, what's the record? Date, source, URL, publication, author. Building a clean citation record during a fact-check session takes time. AI can help format and organize citations as you work.

Claim analysis: When you find conflicting sources, AI can help analyze what explains the discrepancy and which source has more authority for this specific claim type.

1. Claude (claude.ai)

Claude is most useful in the fact-checker's workflow for claim identification, analysis, and the reasoning work that happens after you've found sources and need to make sense of them.

For claim identification, you can paste a section of a manuscript into Claude and ask it to list every checkable factual claim in that section: statistics, historical dates, quotes attributed to specific people, claims about events, references to studies or reports. Claude does this systematically in a way that's faster than going sentence by sentence manually, and it distinguishes between factual claims and characterizations or opinions that don't require the same verification approach.

For analyzing conflicting sources, Claude is useful for reasoning through why two credible sources might give different numbers for the same fact. It doesn't always know which source is correct, but it can explain what methodological differences might produce different figures, which helps you decide what to investigate further and what to disclose to an editor.

The critical warning: never use Claude to confirm a fact. Claude's training data has a cutoff and Claude can and does generate plausible-sounding information that's wrong. Use Claude to identify claims, reason about sources you've found, and help structure your fact-checking record. Don't use it to confirm whether a specific claim is true.

Best for: Claim identification and triage, source conflict analysis, structuring fact-check records, and reasoning about what type of verification a specific claim requires. Pricing: Free tier available; Claude Pro at $20/month.

2. Perplexity

Perplexity is the most directly useful tool in a fact-checker's workflow among the options on this list. It searches the current web and returns cited results, which is exactly what source-finding in fact-checking requires.

The key feature is that Perplexity shows you where each piece of information comes from. For fact-checking, that source trail matters. When you search for the current enrollment number at a specific university, you get a number with a citation. You can verify whether the citation is a primary source, an institutional website, or a media aggregation. That tells you whether you need to go find the original source or whether what Perplexity found is sufficient for your publication's standards.

Perplexity handles current information, which matters for fact-checking recent articles. Statistics, organizational details, and official positions change. Perplexity reflects recent information and shows you the publication date of its sources, which helps you catch cases where the author used accurate information that's now out of date.

For building citation records, Perplexity's sources are a fast starting point. You can copy the cited URL into your fact-check record and verify it directly. That's faster than a cold Google search for each claim.

Perplexity Pro's Advanced Search mode is worth using for complex factual claims where you want more thorough source retrieval. At $20/month, the combination of Claude and Perplexity covers most of the analytical and source-finding needs in a professional fact-checking workflow.

Best for: Source finding for specific claims, current information verification with citations, and quick lead research that points you to primary sources. Pricing: Free tier available; Perplexity Pro at $20/month.

3. Glean

Glean is on this list for fact-checkers working inside large organizations with significant institutional knowledge: major news organizations, academic publishers, research institutions, and large content companies.

The specific value for fact-checkers in these settings is retrieval of prior fact-checks and institutional knowledge. A fact-checker at a publication that has been publishing for 20 years shouldn't have to re-verify the same institutional background facts from scratch every time an author refers to them. Glean indexes the organization's internal documents, prior work, and reference materials, and makes them findable in seconds.

For research teams at large organizations, Glean also retrieves information across tools: SharePoint, email, Confluence, Google Drive, whatever combination of internal tools the organization uses. A fact-checker looking for a specific report that the research team produced two years ago doesn't have to remember where it was filed.

The data access controls are relevant for editorial organizations. Fact-check records often contain information about unpublished reporting that should be accessible only to authorized staff. Glean's permissions-aware retrieval means staff see what they're authorized to see.

Glean is enterprise-only with custom pricing. It's not relevant for independent fact-checkers or small operations. For large organizations where institutional knowledge retrieval is a daily bottleneck, it's worth evaluating.

Best for: Large news organizations and publishers where institutional fact-check records, reference materials, and prior reporting need to be searchable across the organization. Pricing: Enterprise only; custom pricing.

Practical guidelines for AI in a fact-checking workflow

What to use AI for:

• Identifying discrete checkable claims in a manuscript
• Finding candidate sources quickly for each claim
• Analyzing conflicting information to understand why sources differ
• Formatting and organizing citation records
• Background research to understand a topic well enough to fact-check it effectively

What not to use AI for:

• Confirming that a specific claim is true
• Treating AI-generated information as a verified source
• Replacing verification against a primary source with an AI summary

The distinction is between AI as a research assistant that finds things for you to verify, and AI as a verification tool that tells you whether things are true. The first use is appropriate and saves real time. The second use is a verification failure.

Frequently asked questions

How do you document AI use in a fact-check record?

Note where AI was used in the research process: "Claim identified via Claude analysis; source found via Perplexity search; verified against [primary source URL]." What matters is that the record shows the claim was verified against a primary source, regardless of how you found that source. AI is part of the research process, not part of the verification record.

What types of claims are AI tools least reliable for?

Specific numerical claims, historical dates, organizational details, and quotes attributed to specific people. These are also the claims most likely to appear in articles and most likely to be wrong in subtle ways. Use AI to find candidate sources for these claims, then verify directly against the primary source. Never use AI output itself as the verification for a specific number or attributed quote.

How does AI fit into newsroom fact-checking at publications with formal standards?

Most publications with formal editorial standards haven't published official AI use policies for fact-checking. The operational standard should be: AI can assist the research and organizational parts of fact-checking, but the verification must still be against a primary source that the fact-checker has directly reviewed. Any AI-assisted fact-check should be documentable as meeting the same standard as a fully manual fact-check.