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An AI assistant can give you a fluent, well-formatted, confident answer that is wrong. That is true of a command, a config value, a version claim or a cited source. Confident wording tells you nothing about whether the answer is correct. This guide explains why the errors happen and gives you a seven-step check to run before you paste anything into a terminal, editor, ticket or production system.
Why a wrong answer can sound right
OpenAI’s September 5, 2025 research article, “Why language models hallucinate,” describes hallucinations as plausible but false statements. It argues that standard training and evaluation procedures reward guessing over acknowledging uncertainty. The same article says some questions are hard to answer reliably because the information is unavailable, the question is ambiguous, or the model’s reasoning is limited.
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In practical terms, the model is producing a likely-sounding continuation. It is not reporting a fact it has independently checked. When it lacks something, such as your exact version or the full error output, it may fill the gap with something plausible instead of saying so.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesNIST’s Generative AI Profile avoids the more anthropomorphic words. It defines “confabulation” as “the production of confidently stated but erroneous or false content (known colloquially as ‘hallucinations’ or ‘fabrications’).” NIST describes this as a risk-management profile. It is not a binding legal standard, and the version surfaced was a draft. “Confidently generated error” says the same thing in plain language.
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What the errors look like
OpenAI’s user guidance lists the kinds of mistakes to expect:
- incorrect definitions, dates and facts;
- fabricated quotes, studies and references;
- overconfident answers to ambiguous or complex questions.
OpenAI’s family guide adds three conditions where errors may be more likely: the question is ambiguous, it is very complex or specific, or it depends on recent information. It also warns that an answer with links can still misread a source, mix up details, misquote, or fill a gap with plausible-sounding text. A link is therefore not proof.
These are OpenAI’s statements about its own products. The underlying risk is described by NIST for generative AI generally, but check each provider’s own documentation for its specific advice.
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The “before you paste” check
This is a practical workflow built from the source-checking principles above. It is not a validated universal standard. OpenAI’s Help Center page “Does ChatGPT tell the truth?” says: “Always verify quotes, data, technical information or references to external documents.” The steps below put that into practice for technical work.
1. Mark the claims that could hurt you if wrong
Split the answer into its parts: factual statements, commands, code, configuration values, version claims and cited sources. A polished explanation can mix sound and unsound details. Concentrate your checking on anything destructive, irreversible, security-relevant or hard to undo, such as deletes, permission changes, firewall rules, registry edits, database migrations and credentials handling.
2. Open every cited source
Treat citations as leads to inspect. Confirm that the page exists, that it is the primary or otherwise reliable source (the vendor’s documentation, the project’s repository or release notes, the standards text), and that it says what the answer claims. Check the date and the exact wording of any quote.
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3. Match the environment
Confirm the exact product, library, operating system, language or runtime, and version against authoritative documentation. If the answer depends on something that changes, such as a recent release, a deprecated flag or a renamed setting, look up the current state yourself. The cited help pages give the general source-checking principle. Version matching is operational advice for technical readers, not a detailed official protocol.
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Ask what context would change the answer: the full error output, your config, your version, your constraints, or an unstated premise in your question. If you do not have those facts, either get them or treat the answer as provisional. Ambiguity and unavailable information are among the reasons OpenAI’s research gives for answers that cannot be reliable.
5. Test safely before applying
- Run code and commands in a disposable environment, such as a virtual machine, container or scratch directory, or on a non-production copy.
- Use a dry-run or preview option where the tool offers one.
- Read destructive operations line by line, and check which user and permissions the command runs with.
- Make sure you can roll back with a backup, snapshot or version control before you change anything real.
This is sound engineering practice. None of the sources found validates it as a specific procedure for AI-generated output.
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6. Sanitize the prompt
Replace credentials and identifiers with placeholders. Remove passwords, authentication codes, proprietary content and other sensitive details before you submit a request. OpenAI states this in guidance about additional safety checks for certain biological and cybersecurity requests. That page is not a full privacy policy for every service. Retention, training and enterprise terms differ between providers and plans, so read the terms of the service you use and follow your employer’s rules.
7. Escalate high-consequence decisions
If a wrong answer could cause security, financial, legal, safety or production harm, route the decision through the responsible specialist and your authoritative process. OpenAI’s guidance recommends critical verification of important information. Its GPT-4 announcement likewise says the right protocol should fit the specific use case.
How to read vendor accuracy figures
Vendors publish error-rate numbers, and they are easy to misread. They describe particular models on particular tests. They do not tell you whether the answer in front of you is right. OpenAI’s GPT-5 System Card (2025) reports the following:
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| Reported figure | What it actually measures |
|---|---|
| 26% smaller hallucination rate, GPT-5-main versus GPT-4o | A comparison on the system card’s described factuality evaluation. It is not “GPT-5 is 26% accurate” and not a guaranteed reduction in everyday use. |
| 65% smaller hallucination rate, GPT-5-thinking versus o3 | A comparison tied to that card’s evaluation setup and model versions. |
| 75% agreement | How closely OpenAI’s automated factuality grader matched human assessments. It measures the grader, not the model’s accuracy. |
The card’s evaluation sets were chosen to be challenging. They include factuality-heavy prompts, prompts that users previously flagged, and high-stakes questions. OpenAI says they are a research signal and do not reflect production prevalence or the average user’s experience. The figures belong to the named models and methods, and newer system cards may supersede them.
If you compare two assistants, use the same axes for both:
- correctness against primary documentation;
- whether source links exist and actually support the claim;
- fit to your exact version and context;
- how clearly the answer admits missing information;
- whether the code can be tested safely.
Do not treat refusal rates, grader agreement, claim-level error rates and response-level error rates as interchangeable.
The Bottom Line
Treat an AI answer as a draft written by someone who never admits doubt. Check the claims that could hurt you against primary documentation for your exact version. Test in a reversible environment, and keep secrets out of the prompt.
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