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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesLarge language models can produce fluent, convincing answers that are false. Their wording—and even a visible explanation of how they reached a conclusion—is not proof that the answer is correct. Treat an LLM response as a set of claims to verify, and increase the level of review when an error could cause harm.
Why can an LLM sound convincing and still be wrong?
An LLM generates likely continuations from patterns learned during training; it does not automatically look up or prove each detail it states. That can produce a plausible answer without a dependable factual basis for every claim. OpenAI describes these plausible but untrue statements as hallucinations and argues that evaluation systems can encourage guessing when they reward exact answers but penalize abstaining (OpenAI, “Why language models hallucinate,” September 5, 2025).
The question may not contain enough information
A prompt can be ambiguous, omit a relevant fact, or depend on a date, location, edition, or version. In those cases, there may be no single answer justified by the information provided. Ask what assumptions the answer relies on and what missing details could change it. A confident response does not settle an underspecified question.
Errors can compound across steps
A multi-step answer can depend on a faulty premise, arithmetic slip, or mistaken intermediate conclusion. Coherent final prose can conceal those errors. OpenAI’s research on mathematical reasoning distinguishes judging a final outcome from evaluating intermediate steps, and studies step-level feedback on math problems (OpenAI, “Improving mathematical reasoning with process supervision,” May 31, 2023).
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A reasoning transcript is not a proof
A model’s displayed chain of thought may be incomplete or may not faithfully describe what caused its answer. Anthropic tested faithfulness by intervening on stated reasoning, while OpenAI’s o1 system card also cautions that chains of thought may not be fully legible or faithful (Anthropic, “Measuring faithfulness in Chain-of-Thought reasoning”; OpenAI, “OpenAI o1 System Card,” 2024). Such an explanation can help you inspect a response, but it cannot certify it.
What does the evidence say about guessing and abstaining?
One OpenAI illustration from its 2025 article reports these SimpleQA results for two named models. The figures belong to that evaluation example; they are not general error rates or a statement of current performance in ordinary conversations.
| Model in the example | Accuracy | Errors | Abstention |
|---|---|---|---|
| gpt-5-thinking-mini | 22% | 26% | 52% |
| o4-mini | 24% | 75% | 1% |
The contrast illustrates why accuracy alone can hide an important difference: a system may answer more often but also make more errors, while another may abstain more. The numbers do not rank models for other tasks; they show why evaluation should consider errors and appropriate uncertainty, not just how often a model gives an answer (OpenAI, “Why language models hallucinate,” September 5, 2025).
How to check an LLM answer
- Break it into claims. Separate names, dates, quantities, causal statements, recommendations, and assumptions. Identify which claims matter most if they are wrong.
- Open the cited sources. Check that each reference exists, is authoritative for the question, is current enough, and actually supports the attached claim. A citation generated by a model is only a lead until you inspect it. OpenAI’s o1 system card describes references that appeared questionable on inspection (OpenAI, “OpenAI o1 System Card,” 2024).
- Prefer primary evidence. For laws, policies, specifications, and current procedures, look for the responsible official source. For a research finding, consult the paper or its original publisher instead of relying only on a model’s summary.
- Recompute what can be checked. Redo arithmetic, unit conversions, dates, and simple logical implications independently. For complex calculations, use a calculator, spreadsheet, or validated code you control, and check both the inputs and assumptions.
- Check the question’s premises and scope. Confirm that key terms are being used consistently and that the answer fits the relevant geography, date, version, or edition. Ask for clarification when any of those details could change the result.
- Use a second pass as a helper. You can ask the model to identify assumptions, possible counterexamples, or claims needing verification; another model may surface issues you missed. But a second generated answer is not independent proof. A Chain-of-Verification study reported improvements on its evaluated tasks, which does not establish universal reliability (“Chain-of-Verification Reduces Hallucination in Large Language Models,” 2023 preprint).
How much verification is enough?
Match the effort to the consequence of being wrong. A low-impact explanation may need only a quick check of its central claim. If a decision could cause substantial harm, verify the relevant facts against primary evidence and seek qualified human review or another safeguard designed for that use case. OpenAI’s guidance on model limitations emphasizes grounding or review for high-stakes outputs (OpenAI, “GPT-4 research,” 2023). A checklist or extra model pass can reduce some risks, but none guarantees that every error will be caught.
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