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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Check an AI answer claim by claim, not as a block of trusted prose. For each important statement, verify that its cited source exists, read the relevant passage to see whether it supports the exact wording, and compare it with independent, credible evidence. For consequential claims, trace the information to the original study, data, law, policy, or quotation when possible.
1. Break the answer into claims worth checking
Separate a paragraph into individual statements that could be verified. A single sentence may combine a date, a statistic, a causal explanation, and a claim about a current rule; each may require different evidence.
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Mark names, dates, quotations, statistics, causal claims, and statements about changing products or policies. Then prioritize what is consequential, surprising, unusually precise, or central to a decision. You do not have to verify every incidental phrase with equal effort, but make clear which important claims you did check.
2. Check each AI citation for existence and support
A citation has two separate tests: is the cited work real, and does it support the claim attributed to it? Search the title or identifier in an appropriate library catalog, journal, publisher, or official site. Confirm the author, title, publication, date, and identifier against the source itself.
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Then open the work and read the relevant passage, not just the search-result snippet or abstract. Check whether the AI preserved qualifications, scope, and context. A genuine paper may be cited with the wrong details, may not say what the answer claims, or may support only a narrower statement. A working URL alone is not proof of support.
A 2023 study of 636 citations in 84 ChatGPT-generated literature reviews across 42 topics found that 55% of GPT-3.5 citations and 18% of GPT-4 citations in its tested set were fabricated; among real citations, 43% and 24%, respectively, contained substantive errors. Those figures describe that study’s models, prompts, and sample—not current products or the general accuracy of AI answers. They illustrate why existence and support must be checked separately. Read the study in the Journal of the Medical Library Association.
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3. Read laterally and look for independent corroboration
Do not assess an unfamiliar source only by how polished or authoritative its own page looks. Leave it and search for what other credible sources say about its expertise, ownership, reputation, and relationship to the underlying evidence. Then look for sources that independently confirm or challenge the claim.
Oregon Department of Education guidance recommends using credible human-authored sources and finding at least two additional sources that confirm or challenge an original. That is a practical information-literacy recommendation, not a guarantee that two sources settle a question. Oregon’s Navigating Now toolkit gives this guidance on pages 28–29.
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Count evidence, not webpages. Several articles repeating the same press release, dataset, or study are not several independent confirmations. A 2024 review of lateral-reading instruction describes the practice as leaving an unfamiliar site to learn about its credibility from other sources and reports improved digital evaluations in interventions from elementary school through adulthood; the reviewed abstract does not provide an effect size. See the review in Current Opinion in Psychology.
4. Trace claims to primary evidence when the stakes warrant it
For a study, report, law, official policy, dataset, or quotation, find the original and inspect the relevant section whenever practical. A summary or secondary account can help you locate evidence, but the original is the better place to confirm what was actually measured, said, or required.
- Statistics: Identify who collected or published the number, the year, the place and population covered, and the measure used. Preserve those limits when repeating it.
- Studies: Check the sample, methods, and outcome relevant to the claim. Do not convert a study result into a universal rule.
- Quotations: Locate the original wording and context; do not rely on an AI-generated quotation or a page that repeats it without attribution.
- Current rules or products: Check a current authoritative source and note when you checked it. A past policy or feature description may no longer apply.
5. Compare evidence on the dimensions that matter
When sources appear to conflict, compare the evidence behind them rather than counting how many pages take each side.
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- Independence: Does one source rely on or repeat another?
- Expertise and authority: Is the source qualified for this specific subject?
- Proximity to evidence: Is it the original data or document, or a summary?
- Date: Does it cover the same time period as the claim?
- Definitions and methods: Are the sources measuring or defining the same thing?
- Population and geography: Do they refer to the same people, place, or jurisdiction?
- Claim-level support: Does the cited passage actually establish the wording being checked?
These checks can reveal that sources are not really disagreeing: one may describe a different year, population, definition, or method. If they do conflict on the same question, retain that uncertainty rather than selecting the answer that sounds most confident.
Credibility judgments about a source and verification of a claim’s accuracy are distinct reasoning tasks, according to the summary of a 2026 article record in ERIC. The record summary does not support more detailed claims about the study’s findings. See the ERIC record.
6. State what the evidence supports—and what it does not
Write with certainty proportional to the evidence. Attribute figures to their source and date, retain relevant limits, and distinguish a confirmed fact from an interpretation. If evidence is missing or sources remain in conflict, say so plainly; do not turn an AI answer into a fact simply by repeating it.
A 2024 study record reports that participants detected inaccuracies better when identical information appeared as static text than in a conversational-agent format. This suggests presentation can affect credibility judgments, but the record does not establish that every reader or interface behaves the same way. See the PubMed record.
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