AI can produce answers that sound polished and certain while containing false facts, invented quotations, or citations that do not support the claim. Reduce the risk by asking a precise question, checking each important claim against its original source, and verifying exact details independently. Treat confidence and fluent wording as style—not evidence.
What an AI hallucination is—and why confidence is not proof
An AI hallucination is a plausible-sounding statement that is false or unsupported. It can be a wrong date or definition, a fabricated study or quote, a citation that does not exist, or an overconfident response to a question that is ambiguous or too complex to answer reliably. OpenAI’s Help Center guide to whether ChatGPT tells the truth describes these failure modes and warns that a confident tone does not guarantee accuracy.
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Some questions do not have enough information for a reliable answer. OpenAI’s September 5, 2025 article, “Why language models hallucinate”, argues that a system should sometimes express uncertainty or ask for clarification rather than guess. No checking routine can make every answer correct; the goal is to catch errors before relying on them.
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A repeatable method for checking an AI answer
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Make the question specific
State the relevant location, time period, version, audience, and constraints. If a response depends on information you have not supplied, provide it or ask the assistant what is missing. For example, replace “What are the rules?” with “What are the current rules for [specific activity] in [jurisdiction], and what official source states them?” A narrower question reduces ambiguity, though it does not guarantee a correct answer.
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Ask for sources and uncertainty
Ask the assistant to separate checkable claims, link to sources, identify the publication or update date, and say when it cannot verify something. You can also ask it to flag assumptions and ask a clarification question instead of filling gaps. A requested confidence score—or a confident tone—is not independent evidence that the answer is right.
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Break the response into claims
List the facts that matter: dates, names, definitions, figures, quotations, causal explanations, and recommendations. A single paragraph may mix accurate information with a false or unsupported detail, so assess the important claims individually rather than accepting or rejecting the whole answer at once.
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Open each cited source
Do not stop at a citation or search snippet. Confirm that the page exists, is authoritative for the particular claim, is current enough for the topic, and actually says what the AI attributes to it. A real source can still be irrelevant, outdated, or misrepresented. When possible, trace important claims to primary evidence such as an original study, official dataset, regulator, court document, standard, or named organization.
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Verify details independently
Compare high-impact or disputed claims with another reliable source. Check quotations word for word; confirm numbers alongside their publisher, date, population, geography, and definition; and recalculate arithmetic using the stated assumptions and units. For changing facts, check the source’s publication or update date. Web access can help locate current material, but it does not ensure that the AI interpreted it correctly.
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Decide what to do with unresolved claims
If evidence is missing, contradictory, inaccessible, or too old, do not silently treat the claim as established. Ask a narrower follow-up, request clarification, or leave the point unverified. OpenAI’s 2025 discussion of hallucinations explains why uncertainty or abstaining can be preferable to guessing when evidence is inadequate.
What to verify first
Spend checking time in proportion to the harm a mistake could cause. Prioritize details that affect a decision, can change over time, or would be difficult to detect from wording alone.
- Sources and quotations: Confirm that a cited work exists and that the exact words or finding appear in it.
- Numbers and calculations: Check the original data, date, units, definitions, population, and assumptions; redo the arithmetic.
- Current facts: Verify recent rules, product details, events, or other changing information against a dated, authoritative source.
- Causes and explanations: Check whether the cited evidence supports the stated relationship, rather than merely discussing the same topic.
- Advice with consequences: Verify it with the relevant authoritative source or a qualified professional before acting.
Can AI make up sources?
Yes. AI can produce fabricated citations, studies, references, or quotations. It can also cite a genuine source that does not support the specific statement. Open the source and inspect the relevant text or evidence; do not assume that a plausible title, author, or URL makes the citation valid. OpenAI’s Help Center guidance recommends checking references directly.
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For medical, legal, financial, or safety decisions, AI can help organize questions or summarize material, but it should not be the sole basis for action. Verify the relevant claim with an authoritative source or a qualified professional who can consider the facts of your situation. The sources cited here do not establish that AI output alone is adequate for these decisions.
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What model evaluations can—and cannot—tell you
Published benchmark results describe a particular model, test, method, and publication context; they are not a guarantee about an individual answer. In OpenAI’s GPT-5 System Card, the company reported lower hallucination rates for GPT-5 main than GPT-4o and GPT-5 thinking than o3 on prompts representative of ChatGPT production conversations. The card also reports separate response-level comparisons, measuring responses with at least one major factual error. These are distinct metrics, and neither gives the probability that any particular answer is correct.
OpenAI’s September 2025 article “Why language models hallucinate” provides another example: in one SimpleQA table, gpt-5-thinking-mini had 52% abstention, 22% accuracy, and 26% error, while o4-mini had 1% abstention, 24% accuracy, and 75% error. Those figures describe that benchmark table, not everyday use or all AI systems. The example illustrates why answer rate and accuracy should be considered alongside whether a system appropriately refrains from guessing.
For a broader view of how AI-system claims can be made more verifiable, OpenAI’s 2020 overview of a report co-authored by people from 30 organizations discusses evidence and evaluation mechanisms: “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation”. It is about evaluating AI systems, not a recommendation for a consumer verification product.
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