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Before publishing AI-assisted copy, verify its individual factual claims against suitable evidence—not whether it merely sounds convincing or appears machine-written. Check that each source faithfully supports the claim, that the draft preserves important context, and that the evidence is strong enough for the claim. Keep a record another editor can inspect, and treat AI detectors and media-provenance checks as separate tools, not truth tests.

How to fact-check AI-generated content before publishing

Start by turning the draft into a list of claims that can be checked one at a time. A long paragraph may combine facts that require different evidence: a date, a statistic, a quotation, and a claim about cause and effect should not be treated as one assertion.

  1. Inventory factual claims. Mark dates, figures, quotations, attributions, named entities, causal statements, and descriptions of images or audio. Split compound sentences into separately testable assertions.
  2. Locate original evidence. Use the evidence suited to the claim: for example, a primary document, official dataset, original study, direct statement, or first-hand record. An AI response, search-result snippet, or repeated secondary claim is not itself proof.
  3. Read the surrounding context. Compare the draft with the source, checking dates, geography, definitions, qualifications, and whether the source actually says what the draft asserts.
  4. Evaluate the citation. Ask whether it is faithful to the source, complete enough to preserve the source’s meaning, and sufficient to support the claim. NIST describes these dimensions in its evaluation probes for agentic AI.
  5. Keep an audit trail. For each material claim, record the source title and URL, publication date or version, relevant passage or table, reviewer decision, and any caveat or unresolved issue. NIST describes machine-readable trails linking decisions to supporting documents as one way to make factual grounding inspectable.
  6. Recheck volatile facts near publication. Verify details that can change, such as prices, policies, product capabilities, laws, and schedules. Where relevant, state the date, jurisdiction, or version so readers know the scope of the claim.
  7. Resolve unsupported claims. Find stronger evidence, narrow the wording and attribute it clearly, or remove the claim. Do not use a detector score or provenance badge as a substitute for this editorial decision.
  8. Run a final citation audit. Confirm that every material factual statement has a source, the source supports the wording, quotations are exact, numerical values and dates match, and no important caveat disappeared during editing.

Can AI detectors tell you whether an article is accurate?

No. AI-authorship detection and factual verification answer different questions. A detector classifies text; that result does not establish whether a passage is true or false. NIST’s 2024 GenAI pilot study overview and results, published in June 2025, says its evaluations benchmark detection tools and do not take a position on factuality. It also discusses limitations as generative systems improve.

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Use detection, if relevant to your workflow, only for the question it is designed to address. To decide whether a claim belongs in a published article, inspect the evidence for that claim and apply human editorial judgment.

How to verify an AI-generated image or audio clip

Check provenance and check the depicted event separately. Preserve the original file when possible, inspect any available credentials or supported provenance signals, and record transformations such as cropping or conversion. Then independently verify the subject, date, place, and context using evidence appropriate to the claim.

OpenAI’s provenance guidance describes supported image and audio checks and their limits. A positive result indicates a supported signal associated with OpenAI; it does not establish accuracy, lack of editing, legal ownership, or correct context. A negative result is inconclusive: signals may be absent, unsupported, stripped, or degraded.

C2PA Content Credentials can help establish an asset’s origin and modification history, and validated credentials can make changes to credentialed assets tamper-evident. They complement fact-checking rather than replacing it. Adoption is optional, so a file without credentials is not thereby proven untrustworthy.

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What to do when an AI-generated claim has no source

Do not publish an unsupported claim as fact just because it is plausible, repeated elsewhere, or confidently phrased. Search for evidence that directly addresses it. If that evidence supports only a narrower statement, revise the claim and make its attribution or qualification clear. If the evidence remains insufficient, omit it.

Apply the same standard to quotations, numbers, and descriptions of media: verify against the original record or source, not a citation that merely looks relevant. A source must carry the burden of the specific wording used in the article.

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Choose verification aids by the question they answer

Claim-to-source review, authorship detection, and provenance checks are not interchangeable. Choose an aid based on what it checks, the evidence it exposes, the context it preserves, the media or file types it covers, and how it reports uncertainty or unsupported cases. A factual claim still needs independently inspectable evidence and editorial review.

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