Large language models (LLMs) can produce fluent, confident answers that are false. Neither polished wording nor a visible explanation proves that a claim is true. To check an answer, break it into claims, compare important ones with authoritative sources, redo calculations, and bring in qualified human review when an error could cause serious harm.
Why an LLM can sound right and still be wrong
It generates likely text, not guaranteed facts
An LLM generates continuations based on patterns learned during training. It does not automatically look up and prove every detail it states. As a result, it can produce plausible but untrue claims—a behavior often called hallucination. OpenAI’s explanation of hallucinations also notes that evaluation systems can encourage guessing when they reward a correct answer but penalize abstaining, even when the model lacks enough information. OpenAI, “Why language models hallucinate” (September 5, 2025).
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Questions can be underspecified
A question may omit a necessary fact, use an ambiguous term, or depend on a date, location, edition, or version. If those details change the answer, a model may silently assume one interpretation and respond as if it were settled. Ask what information is missing and which assumptions would change the result.
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A multi-step answer can depend on an incorrect premise, arithmetic operation, or inference. Coherent final prose does not reveal whether every intermediate step is sound. OpenAI’s work on mathematical reasoning distinguishes assessing a final outcome from evaluating individual reasoning steps; its process-supervision study examined feedback on steps in math tasks, not a universal guarantee for other kinds of answers. OpenAI, “Improving mathematical reasoning with process supervision” (May 31, 2023).
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An explanation is not a proof of how the answer was produced
A model’s visible reasoning may be incomplete or may not faithfully describe what led to its answer. Anthropic studied faithfulness by intervening on stated reasoning, and OpenAI’s o1 system card cautions that reasoning traces may not be fully legible or faithful. That does not make every explanation useless; it means a reasoning transcript is not an audit certificate. Anthropic, “Measuring faithfulness in Chain-of-Thought reasoning” (2023); OpenAI, “OpenAI o1 System Card” (2024).
What a benchmark example shows—and what it does not
OpenAI’s 2025 hallucinations article reports a SimpleQA example in which gpt-5-thinking-mini had 22% accuracy, 26% errors, and 52% abstention; o4-mini had 24% accuracy, 75% errors, and 1% abstention. These are reported results for the named models in that particular evaluation, not general error rates for LLMs or predictions about ordinary conversations. The contrast illustrates why accuracy alone can conceal a willingness to guess: the model with higher accuracy in this example also had many more errors and rarely abstained. OpenAI, “Why language models hallucinate” (September 5, 2025).
A repeatable workflow for checking an LLM answer
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Separate the answer into claims
Pull out dates, names, quantities, causal statements, recommendations, and assumptions. Treat each as a separate item to verify, especially if the answer’s conclusion depends on it.
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Open and inspect the cited sources
Ask for references if none are provided, then visit them yourself. Confirm that each source exists, is appropriate and current for the question, and supports the specific claim attached to it. A citation generated by a model is only a lead until checked; OpenAI’s o1 system card describes references that appeared questionable on inspection. OpenAI, “OpenAI o1 System Card” (2024).
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Prefer the source closest to the evidence
For a law, policy, specification, or current procedure, look for the responsible official source. For a research finding, inspect the paper or its original publisher rather than relying only on a model’s summary. A secondary explanation can help with context, but it should not substitute for authoritative evidence when the details matter.
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Recompute calculations and conversions
Redo arithmetic, unit conversions, date differences, and simple logical implications independently. For complex calculations, use a calculator, spreadsheet, or validated code whose inputs and assumptions you control; check those inputs as well as the output.
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Check the question’s premises and scope
Confirm that the prompt supplies enough information and that key terms are used consistently. Check whether geography, version, or date changes the answer. If a missing detail matters, ask for clarification rather than accepting a guess.
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Use a second pass as a prompt to investigate
You can ask the model to identify assumptions, find possible counterexamples, or check a calculation. A separate model may surface issues, too. But a model agreeing with itself—or another model—does not establish truth. Chain-of-Verification research reported improvements on its evaluated tasks; that result does not establish universal reliability. “Chain-of-Verification Reduces Hallucination in Large Language Models” (2023 preprint).
Match the review to the consequences
For a low-consequence task, a quick check of key facts may be enough. As the potential harm grows, so should the independence and rigor of the review: use primary evidence, verify critical claims individually, and seek qualified human review or avoid relying on the model when appropriate. The right safeguards depend on the use case; OpenAI’s guidance on GPT-4 describes limitations and calls for particular care in high-stakes contexts. OpenAI, “GPT-4 research” (2023).
- Routine, reversible use: Check any important factual details and calculations before using them.
- Work or study with meaningful consequences: Inspect authoritative sources, validate assumptions, and have another person review claims that affect the result.
- High-stakes decisions: Do not treat an LLM answer as a substitute for qualified judgment. Use safeguards designed for the decision and have a qualified person review the evidence and conclusion.
What self-checking can and cannot do
Asking a model to verify its own answer can help expose weak premises or prompt a more careful check. Research on process supervision explores feedback on intermediate steps, while Chain-of-Verification reports results on specific evaluated tasks. Neither establishes that self-checking will catch every error or makes consequential claims safe without external validation. OpenAI, “Improving mathematical reasoning with process supervision” (May 31, 2023); “Chain-of-Verification Reduces Hallucination in Large Language Models” (2023 preprint).
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