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What a chatbot is—and what it is not
A chatbot generates likely text from patterns in its training and, in some services, from documents or web results supplied at query time. It does not automatically understand your circumstances, inspect your records, apply the law in your jurisdiction or accept professional accountability for the outcome. The conversational format can hide that distinction.
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The National Institute of Standards and Technology (NIST) calls one important failure mode confabulation: “a phenomenon in which GAI systems generate and confidently present erroneous or false content in response to prompts.” Its Generative AI Profile (NIST AI 600-1, 26 July 2024) also notes that open-ended, long-form answers and specialist contexts are especially prone to inaccurate or inconsistent output. A polished explanation, a chain of reasoning or a list of citations can all be generated without being true.
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- Chatbot: orientation, drafting, summarizing material you provide, brainstorming questions and identifying issues to investigate.
- Authoritative source: the current rule, guidance, evidence or official form you rely on.
- Qualified professional: interpretation of your facts, judgment about trade-offs and responsibility for a consequential decision.
WHO made the health-specific risk explicit in its 16 May 2023 warning: “LLMs generate responses that can appear authoritative and plausible to an end user; however, these responses may be completely incorrect or contain serious errors, especially for health-related responses.” This does not mean every answer is false. It means confidence and usefulness must be demonstrated, not inferred from tone.
How to use AI chatbots without relying on them for expert advice
Start with a low-risk task, make the chatbot show its assumptions, verify any material claim, remove sensitive details and define the point at which a human must take over. The following workflow works across health, law, finance, employment, education and technical safety.
1. Classify the decision before you type
Ask what could happen if the answer is wrong, how quickly the situation could change and whether the information can be checked against a current primary source. Use this scale:
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| Situation | Appropriate chatbot role | Required control |
|---|---|---|
| Low stakes: learning a term, outlining a project, rewriting a non-sensitive note | Explain, summarize, brainstorm or draft | Spot-check facts; no personal or confidential data |
| Moderate stakes: comparing public policies, preparing for a consultation, interpreting a public technical document | Build a question list and identify topics to verify | Open the original documents and check dates, scope and definitions |
| High stakes: symptoms, medication, legal rights, tax or investment action, identity, safety-critical work | Prepare questions or organize information only | A qualified professional and authoritative, current sources remain responsible |
If a wrong answer could cause injury, loss of rights, a significant financial loss or a dangerous delay, treat the chatbot as a preparation tool rather than an adviser. NIST identifies these consequential contexts as heightened-risk settings; WHO’s health guidance specifically says humans should remain in control of health-care systems and medical decisions.
2. Ask for orientation, not a verdict
Prompts that request definitions, options or questions are safer than prompts that ask for a diagnosis or a single action. For example:
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- “Explain this insurance term in plain language and list the questions I should ask my insurer.”
- “Turn this public regulation into a checklist, quoting the section numbers so I can verify each item.”
- “What information would a clinician need to assess this symptom? Do not diagnose me.”
- “List the assumptions behind this calculation and identify which figures I must confirm.”
Ask the system to separate facts from assumptions, identify uncertainty and state the date or jurisdiction that applies. Do not treat an instruction such as “be certain” or “act as my doctor” as a qualification; it changes style, not competence.
3. Request sources, then inspect every important one
A citation is a lead to investigate, not evidence by itself. NIST warns that generative systems can produce false citations and false reasoning in addition to false conclusions. For each claim that could change your decision:
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- Open the cited link rather than relying on the title or quotation displayed by the chatbot.
- Confirm the author or issuing organization, publication or update date, geographic scope, edition and audience.
- Find the exact passage and check whether it supports the claim as stated, including exceptions and conditions.
- Prefer a primary source: a government department, regulator, court, professional body, standards organization, original study or official product documentation.
- Record the source and date in your notes. If the source is unavailable, irrelevant or silent on the point, mark the claim unverified and do not use it for a consequential choice.
For health-related AI governance, WHO recommends evidence of benefit, protection for patients and expert supervision before widespread routine use. Its 2024 guidance on ethics and governance of large multi-modal models is institutional guidance rather than a consumer triage manual, so it should inform governance questions—not replace local clinical advice.
4. Check whether the answer fits your context
Even a genuine source may not apply. Check:
- Location: laws, benefits, prescribing rules and emergency numbers differ by country, state or province.
- Date: guidance, prices, software behavior and eligibility can change; a historic page may be superseded.
- Edition or version: a standard, contract, device model or medication formulation may have materially different requirements.
- Facts: the chatbot may have silently assumed an age, diagnosis, income, deadline, operating system or risk tolerance that is not yours.
- Completeness: a summary can omit contraindications, exclusions, footnotes or procedural steps.
Ask the chatbot to list missing information, then obtain that information from the responsible organization or professional. Never fill an unknown with a guess simply because the answer sounds plausible.
5. Minimize what you disclose
Do not assume that a chat is private. The Federal Trade Commission’s 9 January 2024 guidance on AI companies’ privacy commitments explains that model-as-a-service companies may receive sensitive personal information, including health and financial data, and must honor their stated data-use promises. It does not establish the settings or practices of every service.
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Before entering information, read the provider’s current privacy notice, retention and training settings, deletion controls, access terms and any employer or school policy. Replace identifying details with generic descriptions when possible:
- Use “a 46-year-old adult in the UK” instead of a name, address, patient number or exact birth date.
- Describe a debt by type and approximate range instead of uploading an account statement with account numbers.
- Paste only the relevant clause of a contract after removing names, signatures, addresses and internal identifiers.
- Do not upload passwords, authentication codes, private keys, unpublished business material, medical records or another person’s data without proper authorization.
Redaction is not a guarantee of anonymity. Unusual combinations of dates, employers, locations and events can still identify someone. If the task requires the original confidential record, use a channel approved by the organization responsible for it and ask a qualified professional to review it.
6. Set a human handoff point before acting
Decide in advance what triggers escalation. Hand the matter to an appropriately qualified person when:
- the answer could materially affect health, legal rights, finances, employment, immigration status, housing or personal safety;
- you have symptoms, a diagnosis, medication question or treatment choice;
- the facts are disputed, incomplete or unusually complex;
- the chatbot gives conflicting answers, cannot produce an applicable primary source or invents a citation;
- a deadline, consent, signature, payment or irreversible action is involved;
- you are being asked to keep the decision secret or to bypass a professional, regulator or safety process.
Bring the chatbot transcript only as background. Give the professional the original documents, your actual timeline and the questions you need answered; do not present the generated text as an independent authority.
How to verify a chatbot answer in practice
Use a claim-by-claim audit
Break a long response into individual claims. For each one, label it verified, partly supported, unsupported or not applicable. A source that confirms one sentence does not validate the rest of the paragraph.
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| Audit question | What a satisfactory check looks like | Warning sign |
|---|---|---|
| Does the source exist? | The link opens to the named organization and document. | 404 page, mismatched title or an untraceable quotation. |
| Does it support the claim? | The passage states the same proposition with the same conditions. | The chatbot generalized a limited study or omitted an exception. |
| Is it current? | The publication is current for the decision and has no superseding notice. | Old guidance, undated page or a newer version elsewhere. |
| Does it apply here? | Jurisdiction, population, product version and circumstances match. | Advice from another country, profession or edition. |
| Who is accountable? | A named organization or licensed professional stands behind the decision. | The only “authority” is the chatbot’s generated explanation. |
Use disagreement as a signal, not a tie-breaker
Asking two chatbots the same question can reveal ambiguity, but agreement does not establish truth: systems can repeat the same erroneous premise. If answers differ, return to the primary source or a professional. Do not select the answer that is more detailed, confident or convenient.
Health questions: preparation is useful, diagnosis is not
Health is the clearest case for a human handoff. WHO’s 2023 warning cites biased training data, serious errors in health responses and risks that sensitive information may not be protected. WHO’s six principles for health AI—human autonomy; well-being, safety and the public interest; transparency and intelligibility; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability—are sector principles, not a substitute for the laws or professional standards in your jurisdiction. The WHO report published 28 June 2021 says humans should retain control of health-care systems and medical decisions.
A chatbot can help you translate a laboratory term, make a symptom timeline or draft questions for an appointment. It cannot examine you, verify your complete history, account for interactions or assume clinical liability. Do not use an AI answer to delay urgent help. Contact local emergency services or follow official urgent-care guidance for your location when the situation may be an emergency; there is no single universal triage rule that can safely replace local services.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Legal, financial and safety decisions
Legal matters
Use a chatbot to define terminology, organize documents and create questions for a licensed lawyer or the relevant court or agency. Verify every deadline, filing requirement, eligibility rule and remedy against the current official source. A general explanation may omit jurisdiction, procedural posture or an exception that changes the result. Never file, sign away rights or miss a deadline solely because a chatbot said an action was safe.
Financial matters
It can explain a public definition or help build a list of questions for a regulated adviser. Verify rates, tax treatment, fees, eligibility and risk disclosures with the institution, regulator or current official documentation. Do not disclose account credentials, full statements or identifying numbers. A model cannot know your complete objectives, liquidity needs, debts, obligations or tolerance for loss unless a qualified adviser assesses them—and even then, the adviser remains responsible for the recommendation.
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Safety-critical and technical work
For electrical, industrial, laboratory, cybersecurity or physical-safety tasks, use the chatbot to locate relevant sections of an approved manual or standard, then have an authorized person confirm the procedure. Check model numbers, revisions, limits, lockout requirements and local rules. Stop if the answer conflicts with a manufacturer manual, workplace procedure or safety officer. Never bypass a guard, interlock or approval step to test a generated suggestion.
Privacy and governance checks for workplaces and schools
Organizations should define which information may be entered, which services are approved, how logs are retained and who reviews outputs. A staff member’s personal subscription may not meet a confidentiality, records-management or sector requirement. Ask the data owner whether a document can be processed, whether contractual terms cover it and how deletion is verified.
WHO Director-General Dr Tedros Adhanom Ghebreyesus warned in 2023: “While WHO supports the appropriate use of technologies, including LLMs, to support health professionals, patients, researchers and scientists, there is a concern that caution that would normally be exercised for any new technology is not being exercised consistently with LLMs.” The operational lesson is to apply the same approval, audit and accountability controls used for other consequential tools.
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- What is the worst realistic consequence if this answer is wrong?
- Can I check the material claim in a current primary source?
- Did I remove names, identifiers, confidential content and unnecessary detail?
- Did I specify location, date, version and assumptions?
- Is a qualified person still responsible for the decision?
- Have I preserved the original document or evidence instead of relying on a summary?
- Would I act differently if the chatbot’s confident wording were removed?
If you cannot answer these questions satisfactorily, stop at orientation and obtain human advice.
What the available evidence does—and does not—show
There is no single, broadly applicable chatbot-accuracy percentage that can responsibly answer whether a system is “reliable.” Performance varies by model, task, domain, language, prompt and date. A regional health-system survey illustrates why context matters: in the WHO European Region’s 2025 release, 86% of 50 responding Member States identified legal uncertainty as a primary barrier to AI adoption, 78% cited financial constraints and 8% reported having liability standards for AI in health. These are country survey results, not chatbot error rates or measures of worldwide public opinion. See WHO/Europe’s 19 November 2025 release for the scope.
The defensible conclusion is procedural rather than numerical: use chatbots where orientation saves time, verify claims that matter, protect information and transfer decision authority to a qualified human whenever the stakes justify it.
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