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Pause before relying on it. A confident tone is not proof: OpenAI warns that ChatGPT can sound certain while wrong, and Anthropic says Claude should not be treated as a singular source of truth. Isolate the disputed claim, check the evidence behind it, and get qualified human review before acting on consequential advice. If the agent used tools or took action, inspect what happened—not just its final response.
What to do first
- Pause. Do not copy, forward, or act on the disputed claim as though confident wording verified it.
- State the exact claim. Separate the factual statement from the agent’s explanation, confidence language, and recommendation. A specific claim is easier to check than a whole answer.
- Trace the evidence. Open the cited original pages rather than relying on the agent’s summary. Check publication dates, definitions, scope, and surrounding context. If no sources are provided, look for authoritative primary evidence appropriate to the claim.
- Test whether the evidence supports the claim. Ask whether the source directly establishes that particular point, whether relevant context is missing, and whether the evidence is sufficient for the conclusion. NIST describes these dimensions as faithfulness, completeness, and sufficiency.
- Raise the review standard when the stakes are high. For health, legal, financial, safety, or other consequential decisions, seek qualified human review and independent reliable evidence; do not use the agent as the sole authority.
- Audit any actions. If the agent used tools, submitted information, changed files, or otherwise acted, inspect the relevant tool history and outcomes. Check any downstream decision or action that may have depended on the wrong answer.
- Correct the record. Once the error is verified, correct the answer and any record or decision that relied on it. The appropriate notification or correction process depends on the situation; there is no universal procedure established for every case.
- Improve the next attempt. Ask the agent to identify uncertainty, show support for each important claim, and ask clarifying questions instead of filling gaps with guesses. These steps can make review easier but do not guarantee accuracy.
How to verify the answer
Follow citations to their original sources
A citation is a lead to evidence, not proof that the answer is correct. Open the source itself and find the passage, data, or official guidance relevant to the claim. Confirm that it refers to the same people, product, place, time period, and meaning as the answer. A source can be genuine yet fail to support the sentence attached to it.
Check for missing context and outdated information
Look at the source’s date and scope, and read enough surrounding material to see whether the agent left out a qualification or exception. For changing topics such as software features, policies, prices, and regulations, an old source may no longer establish the current answer. If evidence is ambiguous or incomplete, treat the claim as unresolved rather than assuming the confident version is right.
Use sources suited to the claim
Prefer a primary source when one exists: for example, official documentation for a product feature or a relevant government authority for a public rule. Secondary explanations can help with context, but should not substitute for the underlying evidence when a decision depends on exact wording or current facts. The sources reviewed do not define a universal number of citations that makes an answer reliable.
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What changes when the stakes are high
The cost of an error should determine how much independent review you require. A minor factual correction may need only a source check; a decision affecting health, legal rights, money, safety, or other important interests warrants qualified human review. OpenAI cautions that confidence is not reliability, while Anthropic advises users not to rely on Claude as a singular source of truth and to scrutinize high-stakes advice.
Do not treat a second AI answer as independent confirmation merely because it agrees. Look for evidence independent of the original response and, where the consequences justify it, a qualified person who can assess the relevant context.
Rank #2
If the agent already took action
An agent’s final message may not reveal every step it took. NIST notes that agent workflows can involve multi-step tool use, making visibility into evidence and decisions important. Review the relevant tool trail—such as searches, submitted data, file changes, or other actions—and identify what followed from the incorrect claim. Then verify and correct affected records or decisions through the appropriate process for that system or organization. No single incident-response route applies to every tool, workplace, or consequence.
Why an AI can sound certain and still be wrong
Language models can produce plausible but false statements. OpenAI’s explanation, “Why language models hallucinate” (September 5, 2025), argues that accuracy-focused scoring can reward guessing instead of admitting uncertainty. It says that asking for clarification or indicating uncertainty is preferable to giving confident information that may be incorrect. That is OpenAI’s account of a failure mode, not a complete explanation of every model’s errors.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOpenAI’s reported SimpleQA comparison illustrates the trade-off in that specific evaluation: gpt-5-thinking-mini had a 52% abstention rate, 22% accuracy rate, and 26% error rate; o4-mini had a 1% abstention rate, 24% accuracy rate, and 75% error rate. OpenAI said the error-rate difference was consistent with strategic guessing under uncertainty. These are results from that reported benchmark comparison, not estimates of how either model will perform on an individual task or all kinds of questions.
OpenAI’s GPT-5 System Card also reports model- and benchmark-specific reductions in hallucinations and major factual errors against named baselines. Those vendor-reported comparisons do not establish that a newer model cannot make a confident mistake or that a particular answer is reliable. Even stronger performance on an evaluation is not a substitute for checking an answer that matters.
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What to ask the agent next time
- “Which parts of this answer are uncertain, and what information would resolve them?”
- “Show the original source for each important factual claim, and explain how the source supports it.”
- “If the available evidence is insufficient, say so rather than guessing.”
- “What assumptions are you making? Ask me for missing details before answering.”
These prompts encourage traceability and honest uncertainty; they cannot ensure the answer is correct. OpenAI’s official ChatGPT guidance states, “Confidence isn’t reliability: The model may express high confidence even in incorrect answers.” Anthropic’s Claude Help Center guidance similarly says users should not rely on Claude as a singular source of truth and should scrutinize high-stakes advice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret model accuracy claims
Published evaluation numbers can help describe performance under a defined test, but they do not certify an individual response. OpenAI’s GPT-5 System Card reports comparisons such as a 26% smaller hallucination rate for gpt-5-main than GPT-4o and a 65% smaller rate for gpt-5-thinking than OpenAI o3. It also reports 44% fewer responses with at least one major factual error for gpt-5-main and 78% fewer for gpt-5-thinking compared with the named baselines. These are vendor-reported, methodology-specific comparisons—not guarantees for a user’s task. The system-card page reviewed does not establish a publication year, so these figures are attributed to the card without assigning one.
The same card reports 75% human agreement in assessing factuality for claims extracted by its grader. That figure describes agreement in that evaluation process; it is not a general measure of whether an AI answer is trustworthy. For practical decisions, inspect the particular evidence and context rather than substituting a benchmark result for verification.
What the guidance does not establish
There is no universal confidence cutoff, minimum source count, or correction procedure that applies to every AI error. The right response depends on the claim’s stakes, the quality and relevance of available evidence, whether the agent acted, and whether the answer can be resolved without more information. When those factors remain unclear, do not treat the agent’s certainty as a reason to proceed.
NIST’s Building Evaluation Probes into Agentic AI project, created May 1 and updated May 5, 2026, describes moving beyond “the AI said so” toward understanding what the AI found, where it found it, and how evidence supports its conclusions. The project is marked ongoing; its guidance is useful for thinking about traceability, not a universal incident-response rule.
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