The Tool Desk
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What to do first when an AI answer seems wrong
- Pause. Do not copy, forward, or act on the disputed claim as if its confident tone made it verified.
- Write down the exact claim. Separate the factual statement from the agent’s explanation, recommendations, and confidence language. A specific claim is easier to check than a long answer treated as one unit.
- Trace the evidence. Open cited original sources rather than relying on the agent’s summary. Check the publication date, scope, definitions, and surrounding context. If no sources are provided, look for authoritative primary sources appropriate to the claim.
- Test whether the evidence supports the claim. Does the source directly say what the answer says? Is important context missing? Is the evidence sufficient for the conclusion, or does the answer go further than the evidence allows? NIST uses the related evaluation qualities of faithfulness, completeness, and sufficiency in its agent-evaluation guidance.
- Choose a review level based on the stakes. For a low-consequence error, checking a reliable source may be enough. For medical, legal, financial, safety, or other consequential matters, seek qualified human review and independent reliable evidence; do not use an AI agent as the sole authority.
- Check actions as well as words. If the agent searched, submitted information, changed files, or took another action, inspect what it did and review any decisions or actions that depended on it. A correct-looking final answer does not establish that every step was sound.
- Correct the record once you confirm the error. Fix the answer and any decision, document, or message that relied on it. Who should be notified and how depends on the situation; there is no single correction procedure for every case.
Why confidence is not proof
A fluent, decisive answer can still be wrong. OpenAI’s ChatGPT guidance warns that “Confidence isn’t reliability: The model may express high confidence even in incorrect answers.” Anthropic gives similar user guidance: “Users should not rely on Claude as a singular source of truth and should carefully scrutinize any high-stakes advice given by Claude.” These are practical warnings, not a way to determine whether a particular answer is false; that requires checking its evidence.
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One explanation for confident errors is that a model can produce a plausible answer when it is uncertain. In its September 5, 2025 article Why language models hallucinate, OpenAI argues that evaluations focused on accuracy can reward guessing rather than admitting uncertainty. It says its Model Spec favors indicating uncertainty or asking for clarification over providing confident information that may be incorrect. That is OpenAI’s account of a failure mechanism, not a complete explanation of every model’s errors.
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OpenAI’s reported SimpleQA comparison illustrates why the right response depends on the task and evaluation. In that comparison, gpt-5-thinking-mini abstained on 52% of questions, answered 22% accurately, and erred on 26%; o4-mini abstained on 1%, answered 24% accurately, and erred on 75%. OpenAI says the error-rate trade-off is consistent with strategic guessing under uncertainty. These results apply to those models on that benchmark—not to all AI systems or to the accuracy of an answer you receive.
#1 Best Overall
How to handle different kinds of answer
| Situation | What to do |
|---|---|
| Low-consequence factual claim | Check the claim against a reliable source, correct it if needed, and avoid passing it on as verified until then. |
| High-stakes advice or decision | Do not rely on the agent alone. Check independent evidence and ask a qualified person to review the issue before acting. |
| No citation or only a secondary summary | Find an authoritative primary source suited to the claim. If you cannot establish support, treat the answer as unverified. |
| Cited source that seems relevant | Read the original and check the claim’s date, scope, definitions, and context. A citation is not proof that the source supports the wording. |
| Agent used tools or took action | Inspect the relevant tool activity and outputs, then identify and review downstream actions that may depend on the error. |
| Ambiguous, outdated, or incomplete question | Clarify what information is missing and whether the answer depends on a date, location, definition, or other condition. Do not treat a guess as resolution. |
There is no universal confidence cutoff, required number of sources, or correction route that works for every claim. Use the stakes, quality of evidence, agent involvement, and remaining uncertainty to decide how much review is needed.
What to check if the agent already acted
Agent workflows can involve multiple steps, tools, and autonomous actions, so reviewing only the final response may miss the problem. NIST’s ongoing project, Building Evaluation Probes into Agentic AI, emphasizes visibility into evidence and decisions. Its aim is to move past “the AI said so” and understand what the system found, where it found it, and how that evidence supports its conclusions.
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- Identify which tools, files, forms, or services the agent interacted with, where that information is available.
- Compare the information it used with the original source and check whether the source supports the disputed claim.
- List follow-on actions or decisions that relied on the claim; verify them rather than assuming they are safe because the agent completed them.
- Correct or escalate affected records and communications as appropriate to the context. The reviewed guidance does not prescribe one universal incident-response process.
How to reduce confident guesses next time
Ask the agent to identify uncertainty, name missing information, provide sources for key claims, and ask clarifying questions instead of filling gaps with guesses. Then check the sources yourself, especially when the answer matters. These steps can make an answer easier to review; they do not guarantee that it is accurate.
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OpenAI’s GPT-5 System Card reports that models were trained to acknowledge inability on selected infeasible tasks, and presents model- and benchmark-specific hallucination comparisons. OpenAI reports a 26% smaller hallucination rate for gpt-5-main than GPT-4o and a 65% smaller rate for gpt-5-thinking than OpenAI o3 under the system card’s methodology. 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 evaluation results, not a prediction about an individual answer or a guarantee that a newer model will not make a confident error. The system-card page does not state a publication date in the reviewed page text, so these figures are attributed to OpenAI’s GPT-5 System Card without assigning a year.
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Rank #4
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