The Tool Desk
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Why confidence is not proof
ChatGPT can produce information that is incorrect, misleading, incomplete, or out of date. A fluent explanation, a decisive tone, or a citation label does not establish that a statement is true. OpenAI’s guidance, “Does ChatGPT tell the truth?”, says to use ChatGPT as a first draft rather than a final source and to check important information.
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That warning applies to references as well as claims: a citation may be fabricated, or a real source may not support the statement attached to it. The useful test is not whether the answer sounds plausible, but whether reliable evidence backs the specific claim.
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A practical workflow for checking an answer
- Break the answer into checkable claims. Pull out factual statements, names, dates, figures, quotations, and claims about cause and effect. A long paragraph may contain several claims that need separate evidence.
- Prioritize claims by consequence. Check first anything that could affect safety, money, a decision, publication, or someone’s reputation. OpenAI’s advice is to verify important information rather than treat every answer as equally dependable.
- Open each citation yourself. Follow the link and make sure the source exists and is appropriate for the claim. Read the relevant passage, not just the headline or search result. Confirm that it supports the exact statement—not merely that it discusses the same subject.
- Go back to the original material where possible. Compare quotations with the source text. For a statistic, check the original report or dataset for the figure, publisher, date, population, and context. For technical instructions or claims, consult the relevant documentation. OpenAI specifically advises verifying quotes, data, technical information, and references to external documents in its verification guidance.
- Check date and scope. For information that changes, inspect when the source was published or updated and whether it applies to the relevant location, version, and period. ChatGPT may omit events after a model’s training cutoff unless tools are used; even with a tool or citation, check the source itself.
- Record what the evidence actually supports. If no suitable source confirms a claim, label it unverified or leave it out. If credible sources disagree, explain the disagreement and its limits instead of treating ChatGPT’s confidence as a deciding vote.
What to check in different kinds of claims
Quotations and attributed statements
Find the original speech, interview, document, or transcript and compare the wording. Check who said it and whether surrounding context changes its meaning. If you cannot locate a primary source, do not present the wording as a verified direct quote.
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Statistics and research claims
Look for the original study, dataset, or report. Confirm the number, the population measured, the time period, and any qualifications. A statistic about one group or place does not automatically describe another, and a study’s finding may be narrower than a summary suggests.
Technical instructions and current facts
Use documentation from the relevant product or service, and check that it matches the version, edition, or region in question. For policies, availability, and other changing details, verify the current official source rather than relying on an undated answer.
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When ChatGPT says it wrote something
Do not use ChatGPT’s answer as an authorship test. OpenAI says responses to questions about whether ChatGPT wrote a passage are unreliable and may be made up. Its Help Center article “Can I ask ChatGPT if it wrote something?” states, in that context, “These responses are random and have no basis in fact.”
A simple standard before you rely on an answer
- Verified: A suitable source directly supports the specific claim, with relevant context and date.
- Partly supported: The source supports only a narrower version; qualify the statement accordingly.
- Unverified or contradicted: No reliable evidence supports it, or a reliable source conflicts with it; do not repeat it as fact.
This is a practical checking approach, not a formal scoring system. OpenAI also notes that answers can oversimplify nuanced or ambiguous questions, so make uncertainty visible when the evidence does not justify a single definitive answer.
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