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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsYou cannot make an AI assistant error-proof, but you can reduce the chance of relying on a hallucination by checking the claims that matter. Treat a fluent answer as a draft: separate it into checkable statements, open its sources, confirm that each source supports the specific claim, and independently verify exact details. For medical, legal, financial, or safety decisions, use AI to organize questions—not as the final authority.
What an AI hallucination looks like
A hallucination is a plausible-sounding answer that contains false or unsupported information. It may be a wrong date or definition, a fabricated quotation, study, citation, or reference, or a confident answer to a question that is ambiguous or too complex to answer from the available information. OpenAI’s Help Center cautions that ChatGPT can sound confident even when it is wrong and recommends using it as a first draft rather than a final source: Does ChatGPT tell the truth?.
Fluency, detail, and confidence are not evidence. A citation is not proof either: it may be nonexistent, outdated, irrelevant, or misrepresented. Browsing or search can provide more current material, but the assistant can still misunderstand a source or draw an unsupported conclusion from it.
A repeatable way to check an AI answer
- Break the answer into claims. Mark each fact, date, statistic, quotation, causal statement, recommendation, and other point you might rely on. A paragraph can mix correct and incorrect claims.
- Open every citation you plan to use. Confirm that the page exists and is authoritative for the point at issue. Read the relevant passage yourself; do not rely on the AI’s description or a search-result snippet.
- Match the evidence to the exact claim. Check that the source supports the specific wording, population, geography, date, and conclusion. A source that discusses the same subject may not support the claim attributed to it.
- Prefer primary evidence where available. Look for the original paper, official dataset, regulator, court document, standard, or named organization. For contested or consequential claims, compare independent sources rather than relying on one summary.
- Verify exact details independently. Check quotations word for word. For figures, inspect the publisher, year, population, geography, units, and definition. Recalculate arithmetic and check the assumptions behind it.
- Check whether the information may have changed. For current events, rules, prices, or other changing facts, inspect the source’s publication or update date. A model’s training knowledge may be out of date; web access can help with recency, but it does not guarantee correct interpretation.
- Decide what to do with unresolved claims. If a material point has no adequate support, do not repeat it as fact. Ask a narrower question, seek better evidence, or leave the claim unverified.
How to reduce hallucinations before they happen
Make the question specific
Ambiguous prompts invite an assistant to fill in missing context with guesses. Include the relevant date, location, units, audience, and constraints. If an answer depends on information you have not supplied, ask the assistant to identify what is missing or ask you a clarification question instead of assuming.
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Request evidence and uncertainty
Ask for sources that directly support important claims, and ask the assistant to distinguish what is supported from what it cannot verify. You might prompt: “List the factual claims separately, link a source for each important claim, and flag anything you cannot verify. If the answer depends on missing details, ask me before assuming.” This instruction can make an answer easier to check; it does not guarantee that the citations or claims are correct.
Prefer a careful non-answer to a guess
When evidence is missing or the question cannot be answered from the available information, uncertainty or a request for clarification is more useful than confident invention. OpenAI’s September 2025 article on hallucinations explains that accuracy-only evaluations can reward guesses over appropriate abstention and notes that some real-world questions are ambiguous or unanswerable from available information: Why language models hallucinate.
When an answer needs extra scrutiny
Spend verification effort in proportion to the harm a mistake could cause. Check especially carefully when an answer will influence a medical, legal, financial, or safety decision; when it gives a precise quotation, statistic, date, or calculation; or when it relies on recent or disputed information. For consequential decisions, verify against authoritative sources and consult a qualified professional. The sources cited here do not establish that AI output alone is adequate for these decisions.
AI can still help organize a problem, summarize material, or prepare questions for an expert. Keep those uses separate from treating its recommendation as verified advice.
How to interpret AI accuracy claims
Benchmark figures describe a particular model, test, method, and publication context; they do not tell you whether an individual answer is correct. For example, OpenAI’s GPT-5 system card reports that GPT-5 main had a hallucination rate 26% smaller than GPT-4o’s, and GPT-5 thinking had a rate 65% smaller than OpenAI o3’s, on prompts representative of ChatGPT production conversations. These are relative comparisons in that evaluation, not absolute probabilities that an answer is correct. The system card also reports a separate response-level measure: GPT-5 main had 44% fewer responses with at least one major factual error than GPT-4o, while GPT-5 thinking had 78% fewer than OpenAI o3. That is a different metric from claim-level hallucination rate. See the GPT-5 System Card for the evaluation context.
A separate example in OpenAI’s September 2025 article illustrates why abstention matters: in the SimpleQA table shown there, gpt-5-thinking-mini had 52% abstention, 22% accuracy, and 26% error; o4-mini had 1% abstention, 24% accuracy, and 75% error. Those figures describe that benchmark example, not everyday use or other models. Neither evaluation guarantees that a given answer is accurate.
Checking claims one by one is also a way to evaluate evidence, not a promise of certainty. A 2020 OpenAI overview of a report co-authored by people from 30 organizations describes mechanisms for making claims about AI systems more verifiable; it concerns evidence and evaluation of AI systems, not a consumer product recommendation: Coordinated audits.
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