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Use AI as a drafting assistant, not an authority. When a model may be wrong, make it identify uncertainty, ask for missing context and show sources. Then verify every consequential claim against the underlying documents and obtain qualified human review for legal, medical, financial, safety or other high-stakes decisions.
Why an AI answer can sound certain and still be false
Generative AI produces likely text, not guaranteed facts. It can combine fragments of training data into a fluent statement that was never true, or attach an invented citation or quotation to an otherwise plausible answer. OpenAI calls these failures “hallucinations” and warns that confidence is not a measure of reliability. Its Help Center advises: “Use ChatGPT as a first draft, not a final source.” OpenAI’s explanation of hallucinations describes the problem and why a polished answer still needs checking.
Do not quote a universal hallucination percentage. Error rates vary by model, task, language, date, prompting and evaluation method, and no single authoritative statistic establishes one number for all uses. Qualitative safeguards are more useful than a misleading average.
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A model may also guess because many evaluations reward an answer more than an honest abstention. OpenAI’s discussion of this incentive says its Model Spec favors uncertainty or a clarifying question when an answer could be wrong: “It is better to indicate uncertainty or ask for clarification than provide confident information that may be incorrect.” Read the explanation of why language models hallucinate.
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Start by defining what the answer must get right
Missing context is a reason to pause, not an invitation to fill in the blanks. Before asking for an answer, specify the details that change the result:
- Task and audience: say whether you need a draft, explanation, calculation, checklist or decision brief, and who will rely on it.
- Date: give the relevant “as of” date. Rules, prices, software interfaces and public-office holders can change.
- Geography and jurisdiction: identify the country, state, province, city or regulatory regime.
- Version or edition: name the software release, standard, contract version, product model or publication edition.
- Stakes and constraints: explain whether an error would be inconvenient, expensive, unsafe or unlawful.
- Available evidence: provide the documents, data, links or excerpts the model is allowed to use.
If any of these facts is unknown and could change the answer, instruct the model to ask before proceeding. A useful opening prompt is:
Before answering, list the date, location, version, audience and stakes you are assuming. Ask me for any missing detail that could change the result. Separate verified information, assumptions, uncertainty and questions. Do not guess or invent sources, quotations, figures or legal/medical conclusions. Link each material factual claim to the underlying source.Require the model to separate facts, assumptions and unknowns
Do not ask only for “the best answer.” Ask for an answer that exposes its limits. Request four labeled parts:
- What is established: facts directly supported by the supplied material or a cited primary source.
- Assumptions: details the model had to infer, such as a date, jurisdiction, definition or intended audience.
- Uncertainty: claims that may be outdated, disputed, incomplete or unsupported.
- Questions: information you must provide before a reliable answer is possible.
This format makes a confident-looking completion easier to audit. It also gives you a stopping point: if a missing fact is material, answer the model’s question rather than accepting a guess.
Wording an honest “I don’t know”
Useful language is specific about the limitation and still helps the reader:
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- “I don’t have enough reliable information to answer that.”
- “This is uncertain because the source is missing or the facts may have changed.”
- “Here is what I can verify, and here is what remains unverified.”
- “Before I answer, what date, location or version do you mean?”
- “I can outline the options, but a qualified professional must make the final determination.”
Avoid turning a plausible completion into a factual claim simply because it sounds polished. “Probably” does not repair an unsupported assertion; name the missing evidence instead.
Example: turning a risky prompt into an honest one
Risky: “Tell me the current filing deadline and penalty.”
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The second version does not guarantee correctness. It makes the conditions visible and creates a checkable source trail.
Verify each material claim instead of trusting the citation label
Verification is your responsibility. The UK House of Commons Library says, “AI should be treated as an assistant, not an authority.” Its briefing on artificial intelligence and Parliament supports that distinction.
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- Break the answer into claims. Mark every date, number, quotation, causal statement, requirement, recommendation and named entity that matters to your decision.
- Open the cited source. Do not rely on a title, snippet or citation generated by the model. Confirm that the page exists and is from the claimed publisher.
- Check entailment. Read the relevant passage and ask whether it actually supports the sentence, including its exceptions and conditions. A source that discusses a general rule may not support a specific conclusion.
- Check time and scope. Confirm that the source is current for the requested date and applies to the same country, jurisdiction, audience, product version or edition.
- Cross-check important claims independently. Prefer a primary document, regulator, official statistic, standard, court decision, contract, or peer-reviewed paper. A second AI answer is not independent evidence; it can repeat the same error.
- Record the result. Note whether the claim is supported, contradicted, out of date or unresolved, and edit the draft accordingly.
Use a source hierarchy that fits the claim
- Primary sources: legislation, regulations, court opinions, official statistics, technical standards, product documentation, research papers and the organization’s original announcement.
- Qualified secondary sources: universities, professional bodies, established newsrooms and specialist analyses that identify their evidence.
- Discovery-only material: search snippets, anonymous posts, scraped pages and unsourced summaries. These can suggest where to look, but should not carry a consequential claim by themselves.
For a quotation, locate the exact words in the original transcript, document or recording. For a number, check the unit, denominator, population, measurement method and date. For a recommendation, check whether the source is describing an option or prescribing one.
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Check AI-generated citations, quotations and links
Invented references are a distinct failure mode. A model may produce a real-looking title with a nonexistent URL, combine a real author with a different paper, or quote words that never appeared in the source. Treat every citation as an unverified lead until you open it.
- Copy the title, author, publisher and date into a browser or the publisher’s own search.
- Open the original page rather than a repost or search-result excerpt.
- Search within the document for the quoted wording, figure or defined term.
- Compare the model’s claim with the source’s qualifiers, footnotes, version number and jurisdiction.
- If the source cannot be found or does not support the claim, remove the citation and label the claim unverified. Do not “repair” it by guessing a similar reference.
Ask the model to provide links for factual claims, but remember that a link is evidence only after a person has checked it. Preserve the checked URLs in your notes or draft when another reviewer will need to reproduce the work.
Match verification and human review to the stakes
Not every error deserves the same control. Use a proportionate process, but do not let convenience override foreseeable harm.
| Use case | Minimum control | When to stop and escalate |
|---|---|---|
| Brainstorming, fictional writing or a private outline | Check names, quotes and any real-world details before publication. | Escalate if the draft will be presented as factual or attributed to a real person. |
| Routine technical or workplace explanation | Specify the software version and test commands or procedures in a safe environment; verify against current documentation. | Escalate when the change could delete data, interrupt production or weaken security. |
| Public article, report or educational material | Open every material citation, cross-check important facts and have an editor review the final copy. | Escalate unresolved disputes, statistics, quotations or claims about people and organizations. |
| Legal, medical, financial, employment, safety or compliance decision | Use AI only for a draft or list of questions; verify against authoritative sources. | Stop at the draft and obtain advice from a qualified professional who knows the facts and jurisdiction. |
OpenAI’s responsible-use guidance says to double-check critical facts, seek qualified experts for legal, medical and financial advice, and “keep a human in the loop for important work.” Read the OpenAI Academy guidance. Human review should be substantive: the reviewer needs the underlying sources, assumptions and unresolved questions, not just a request to approve a finished paragraph.
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Apply a formal validation mindset
NIST describes validation as confirmation through objective evidence. Its AI Risk Management Framework resources place validation alongside reliability, explainability and context-appropriate controls. See NIST’s characteristics of trustworthy AI.
In practical terms, validation means testing the answer against evidence and the intended use:
- Reliability: does the process produce the right result repeatedly on relevant cases, or did it succeed once by chance?
- Explainability: can a reviewer follow the assumptions, calculations and sources?
- Context fit: is the level of accuracy, privacy and review appropriate to the harm an error could cause?
- Robustness: does the answer remain valid when a date, input, spelling or version changes?
These checks do not turn a language model into an authority. They make the human workflow more defensible.
Keep a review record that another person can reproduce
For work that may be challenged or reused, record:
- the model and interface used;
- the date and time of the request;
- the exact prompt and important input documents;
- the requested date, geography, version and assumptions;
- each source opened and what claim it supported;
- unresolved uncertainty, rejected citations and corrections;
- the name or role of the human reviewer and the edits they made.
A record prevents “citation drift,” in which a draft is updated but its old evidence remains attached. It also lets a later reviewer repeat the check after a law, statistic, product or model changes.
Disclose AI assistance when the rules require it
Disclosure is governed by the setting, not by a universal formula. Follow the policy of the school, employer, publisher, client, regulator or contract. If disclosure is required, state what the system did and what a person did—for example, that AI generated an initial outline while a human verified sources, revised claims and approved the final text.
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When policy permits and the work is consequential, preserve useful links or logs so an editor, instructor, client or auditor can review the process. Do not claim that a human performed checks that did not happen, and do not present AI-generated quotations or analysis as personal observation.
Compare AI answers or tools on evidence, not polish
When two systems produce different answers, the more fluent one is not automatically better. Compare them on the dimensions that affect trust:
| Dimension | Questions to ask |
|---|---|
| Evidence quality and traceability | Are claims tied to primary or otherwise reputable sources that a reader can open? |
| Recency and scope | Do the sources match the required date, version, geography and jurisdiction? |
| Uncertainty and assumptions | Does the answer identify missing information, conflicts and confidence limits instead of hiding them? |
| Privacy and data handling | What sensitive information would be sent, retained or exposed, and is that acceptable for the task? |
| Bias and perspective | Could the data or framing omit affected groups, competing interpretations or relevant incentives? |
| Human or expert review | Can a qualified person inspect, correct and approve the output before it is used? |
| Fit to the stakes | Does the workflow provide stronger controls where an error could cause material harm? |
Run the same clearly specified task through each option, then compare the evidence and correction effort. Do not treat agreement between two models as confirmation; both may have learned the same incorrect text.
Common failure modes and their fixes
| Failure | Why it happens | Fix |
|---|---|---|
| The answer fills an ambiguity silently. | The prompt omitted a date, location, version or definition. | Require a clarifying question and list assumptions before the answer. |
| A citation looks convincing but is dead or irrelevant. | The model generated a plausible reference rather than retrieving verified evidence. | Open the source, check the passage and remove unsupported claims. |
| A second model “confirms” the first. | Both systems can reproduce the same training-data error. | Check the underlying primary source or ask a qualified reviewer. |
| A true general rule is applied to the wrong case. | Exceptions, jurisdiction or version details were ignored. | Compare the source’s scope and conditions with the actual facts. |
| A draft is used as professional advice. | Fluent wording hides the boundary between explanation and decision. | Use the draft to prepare questions, then obtain legal, medical, financial or other qualified advice. |
| The final copy omits how AI was used. | The writer did not check the applicable policy. | Review disclosure requirements and describe the human verification performed. |
A repeatable honesty workflow
Use this sequence whenever accuracy matters:
- Define the task, audience, date, geography, version and stakes.
- Ask the model to separate established information, assumptions, uncertainty and questions.
- Require links for factual claims and prefer primary documents, regulators, official statistics, standards and peer-reviewed work.
- Open every citation and test whether it supports the exact sentence.
- Cross-check important claims independently; do not count another AI response as independent evidence.
- Escalate legal, medical, financial, safety, employment and other high-stakes decisions to a qualified human.
- Record the model, date, sources, unresolved uncertainty and human edits.
- Disclose AI assistance when a school, employer, publisher, client or law requires it.
The practical rule
AI is most useful when it makes uncertainty visible and helps you find, organize and explain evidence. The moment it cannot show reliable support—or the cost of being wrong is high—stop treating the output as an answer. Ask for clarification, verify the underlying source and put a qualified person in charge of the decision.
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