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Use an AI chatbot as an orientation and preparation tool—not as the person who decides what is medically, legally, financially, or otherwise professionally correct. It can translate jargon, organize facts, draft questions, and point you toward sources. You must verify important claims, protect sensitive information, and hand consequential decisions to a suitably qualified human.

That boundary matters because fluent language is not evidence of expertise. The World Health Organization (WHO) warns that large language models can produce authoritative-sounding answers that are completely wrong or seriously erroneous, particularly in health. The U.S. National Institute of Standards and Technology (NIST) calls confidently presented false output “confabulation.”

What a chatbot is useful for—and what it is not

A chatbot predicts and composes text from patterns in its training and, in some systems, from documents or web results supplied at the time of a conversation. It does not automatically know your complete circumstances, apply the professional rules in your jurisdiction, or accept accountability for an outcome. A polished paragraph can therefore be useful for orientation while still being unsafe as a final answer.

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Good low-stakes uses include:

  • Explaining an unfamiliar term in plain language.
  • Turning a long document into a list of topics to discuss with a professional.
  • Suggesting questions for a doctor, lawyer, accountant, engineer, teacher, or other specialist.
  • Organizing information you already have into a timeline, checklist, or comparison.
  • Showing several interpretations of a concept so you can identify what you do not yet understand.

Those uses help you prepare. They do not transfer decision authority to the model. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published 26 July 2024) explains that generative systems can produce inaccurate or internally inconsistent output, especially in open-ended responses and work requiring contextual or specialist expertise. It also warns that false reasoning and fabricated citations can make an answer seem more trustworthy than it is. Read the profile at NIST AI 600-1.

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WHO states the health-specific concern plainly: “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.” That warning does not mean every response is false. It means confidence, detail, and a helpful tone cannot substitute for verification.

Match your method to the stakes

Before typing, classify what could happen if the answer is wrong. The higher the possible harm, the less you should rely on generated content and the more you need current authoritative sources and a responsible professional.

Task level Examples Reasonable chatbot role Verification and handoff
Low stakes Learning a definition, brainstorming headings, formatting notes Explain, summarize, or organize Spot-check obvious facts; no professional decision is being made
Moderate stakes Understanding a workplace policy, comparing product specifications, preparing questions about a bill Translate language and identify issues to investigate Open the original policy or specification, check its date and scope, and ask the responsible organization when ambiguity matters
High stakes Symptoms and treatment, legal rights or deadlines, investing or borrowing, safety-critical repairs Prepare a concise history, question list, or source checklist Verify each material claim with current authoritative information and have a qualified professional make or confirm the decision
Urgent or irreversible Possible emergency, imminent legal deadline, threat to physical safety, suspected fraud in progress At most, help you write down facts while you seek help Contact local emergency services, an official urgent-care channel, an appropriate authority, or a qualified professional immediately; do not wait for a chatbot conversation

The table is a decision rule, not a measure of a particular model’s accuracy. No general chatbot accuracy percentage is established here; performance changes with the model, prompt, task, domain, and date.

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A safe, repeatable workflow

1. State the decision you are actually facing

Write one sentence describing the outcome you need, not just the topic. “Explain what this laboratory term means” is different from “Tell me whether I should stop a prescribed medicine.” The first is orientation; the second asks for a clinical decision. Naming the decision exposes when a human must remain responsible.

Record your location and the relevant date when rules, prices, policies, or clinical guidance may vary. A request such as “What does this tenancy notice require under the law where I live, as of March 2026?” gives you a verification target. It still does not make the chatbot a legal adviser.

2. Remove identifying and confidential details

Describe a situation generically whenever possible. Replace a full name with “a 42-year-old adult,” remove account numbers, and omit exact addresses, patient identifiers, access codes, proprietary business material, and information about another person that you are not authorized to disclose. Keep only details needed to explain the question.

The Federal Trade Commission (FTC) explains that model-as-a-service companies may receive sensitive personal information, including health and financial data, and must honor their stated privacy commitments. Its 9 January 2024 guidance, “AI Companies: Uphold Your Privacy and Confidentiality Commitments,” is a reminder not to assume that anything entered into a chatbot remains private. Before using a service for sensitive material, read its current data-use terms and settings; do not infer those practices from the quality of its answers.

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3. Ask for orientation, not a verdict

Prompts that request structure are safer than prompts that ask the model to take responsibility. Useful wording includes:

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  • “Explain this term in plain language, then list what a qualified professional would need to know before advising me.”
  • “Turn these notes into questions for my appointment. Do not diagnose or recommend treatment.”
  • “List the issues I should verify in the current official guidance, and mark anything you are uncertain about.”
  • “Give me competing interpretations and the fact that would distinguish between them.”

Ask the system to separate supplied facts from assumptions, identify missing context, and state the date or jurisdiction it is using. These instructions improve the conversation’s usefulness, but they do not guarantee truth.

4. Request sources—and treat them as leads

For every claim that could change your decision, ask for the issuing organization, document title, publication or update date, and a direct link. Then inspect the source yourself. NIST specifically warns that generated citations may be false, so a plausible-looking reference is not proof that a document exists.

Use this verification sequence:

  1. Open the link rather than trusting the displayed title.
  2. Confirm that the page or document exists and is published by the named author, agency, court, professional body, regulator, or other appropriate authority.
  3. Check the publication date, update date, jurisdiction, edition, and scope.
  4. Find the exact passage supporting the chatbot’s claim. Make sure the passage says what the answer says, rather than merely mentioning the same topic.
  5. Check whether later guidance, an exception, or a different local rule changes the result.
  6. Save the source and the date you checked it if the decision may be disputed or revisited.

If the link is broken, the title cannot be found, or the source does not support the statement, treat the claim as unverified. Ask the chatbot to withdraw or rephrase it, but do not let a revised sentence stand without checking a real source.

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5. Prefer primary, current information

For health questions, start with a national health authority, a public hospital or health system, a recognized professional body, or the clinician responsible for your care. For legal questions, look for the relevant court, legislature, regulator, legal-aid service, or a licensed lawyer in your jurisdiction. For finance, use the responsible regulator, a product issuer’s current terms, and an appropriately qualified adviser. For safety, use the manufacturer’s current manual, an official standard, or a certified technician.

WHO’s health guidance calls for evidence of benefit, protection of patients, transparency, rigorous evaluation, and expert supervision before widespread routine use of large language models in health care. Its 2021 principles also say that people should remain in control of health-care systems and medical decisions. Read the principles in WHO’s global report and six guiding principles. These are health-sector principles, not a replacement for the law or professional standards in another field.

6. Test the answer for internal warning signs

Even before external checking, look for signals that the output needs extra scrutiny:

  • It gives a precise number, deadline, dosage, quotation, or case name without a verifiable source.
  • It changes its answer when you ask the same question in different words.
  • It combines rules from different countries, editions, or time periods.
  • It sounds certain despite missing facts that a professional would normally ask for.
  • It cites a source whose title, author, or URL you cannot locate.
  • It invents a mechanism, study result, policy exception, or personal experience.
  • Its explanation is long but never identifies what evidence would prove or disprove the conclusion.

These signs do not prove the answer is wrong; they tell you where to stop treating it as a working fact.

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7. Set a human handoff point before you act

Decide in advance what will trigger professional review. Examples include a new or worsening symptom, a proposed medication change, a legal filing or deadline, a contract you must sign, a tax or investment action with material consequences, a safety repair, or any decision affecting another person’s rights or welfare.

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Bring the chatbot’s output as a list of questions, not as authority. Give the professional the original documents and your relevant facts, and say which points you independently verified and which remain uncertain. The professional can then correct the model’s framing instead of having to discover hidden assumptions inside a confident paragraph.

8. Stop when the situation may be urgent

A chatbot should never delay emergency help. If someone may be in immediate danger, contact local emergency services or follow official urgent-care instructions for your location. For a time-sensitive legal or financial problem, contact the responsible authority or a qualified professional immediately. A model can help you write down times, symptoms, transactions, or questions while you seek help, but it cannot examine a person, preserve a legal deadline, or guarantee a safe intervention.

How to check whether a chatbot’s answer is true

“True” is not one test. Check the claim at the level that matters to your decision.

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Check the claim, not just the topic

Break a paragraph into individual statements. A source may support the general topic while failing to support the specific dosage, exception, date, or causal claim. Copy the material sentence into your notes and ask, “What evidence would establish this exact statement?”

Check authority and fit

An official source can still be irrelevant if it covers another country, an older edition, a different population, or a different product. A professional article can be informative without being an applicable rule. Record who issued it, whom it governs, and when it was last updated.

Check independent agreement

For a consequential claim, look for confirmation from more than one appropriate source that was not copied from the chatbot’s wording. Independent agreement is useful, but it is not a substitute for a professional when the decision is high-stakes. Several webpages repeating the same unsupported sentence do not create evidence.

Ask what would change the answer

A trustworthy explanation should identify conditions that lead to a different result. Prompt: “List the facts that would make your answer change, and tell me which of those facts I need a professional to assess.” If the model cannot identify meaningful conditions, that is a reason to escalate rather than a reason to trust it.

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Health questions require the strongest safeguards

Health is where a fluent error can cause immediate physical harm. WHO’s 16 May 2023 warning lists risks from biased training data, seriously incorrect responses, inadequate protection of sensitive information, and insufficient evaluation. It calls for transparency, expert supervision, and evidence of benefit before routine, widespread use in health care. Read the announcement at WHO calls for safe and ethical AI for health.

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Use a chatbot to translate a discharge instruction into questions, create a symptom timeline, or explain the difference between two terms. Do not use it to diagnose, select or stop treatment, calculate a personalized dose, interpret a dangerous symptom without clinical review, or decide whether urgent care can wait. Share the original report with a clinician; a summary generated by a model may omit a qualifier that changes the meaning.

WHO Director-General Dr Tedros Adhanom Ghebreyesus said in the 2023 announcement: “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 statement supports a supervised-support role, not unrestricted self-care.

Read health content with the six principles in mind: protection of human autonomy; promotion of well-being, safety, and the public interest; transparency, explainability, and intelligibility; responsibility and accountability; inclusiveness and equity; and responsiveness and sustainability. Those principles describe responsible health-system design. They do not tell you what a particular symptom means or replace local clinical advice.

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Privacy: decide what the service should never receive

Privacy risk is separate from answer accuracy. A correct answer can still come from an inappropriate disclosure. Make a “do not paste” list for your work or household:

  • Government identifiers, passwords, authentication codes, and full payment details.
  • Detailed medical records, images containing names or dates of birth, and another person’s health information.
  • Unpublished business plans, customer records, source code under a confidentiality agreement, or privileged legal communications.
  • Exact location and schedule information that could put someone at risk.

Use redaction, aggregation, and fictional placeholders. Keep a copy of the redacted version so you can tell a professional exactly what information was omitted. Review the service’s current retention, training, deletion, account-access, and enterprise controls before entering anything sensitive. The FTC’s guidance concerns providers’ privacy promises and enforcement obligations; it does not establish the settings or practices of any particular chatbot.

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Common failure modes and what to do instead

“It sounded certain, so I assumed it was checked.”

Replace tone-based trust with a source check. Ask for the exact primary document, open it, and locate the supporting passage. If no suitable source exists, label the statement unverified.

“It gave me a citation, so the answer must be researched.”

Generated citations can be invented or mismatched. Verify the document’s existence, author, date, and wording before relying on it.

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“I pasted the whole file to get a better answer.”

Start with a redacted excerpt and a precise question. Add only the minimum context needed. Confirm that you are permitted to share the material and that the service’s current terms fit the sensitivity of the data.

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“The answer was hedged, so it must be safe.”

Words such as “probably” or “generally” do not transfer responsibility. A qualified human still needs to assess a decision that can materially affect health, rights, money, or safety.

“Two chatbots agreed.”

Agreement between systems can reflect the same training patterns or the same error. Compare the underlying authoritative sources, not the number of models producing the sentence.

“I will ask the chatbot first because professional help is inconvenient.”

For urgent or high-consequence situations, convenience is the wrong criterion. Use the chatbot only to prepare a concise account while contacting the appropriate professional or service.

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Prompt patterns that preserve human responsibility

Adapt these templates without inserting unnecessary personal data:

  • Plain-language orientation: Explain [term] for a non-specialist. Separate established facts from assumptions, give the date and jurisdiction that matter, and list questions I should ask a qualified professional.
  • Source audit: For each material claim in your answer, provide the issuing organization, document title, publication or update date, and direct URL. Do not invent a citation; say “not verified” when you cannot locate one.
  • Appointment preparation: Turn these redacted notes into a one-page timeline and questions for my clinician. Do not diagnose, recommend a treatment, or tell me to delay urgent care.
  • Decision boundary: What parts of this question are general information, and what parts require a licensed professional in [jurisdiction]? List the facts that professional will need.
  • Counter-check: Identify the strongest reasons this answer could be wrong, including missing facts, outdated rules, conflicting guidance, or a different jurisdiction.

These prompts improve the division of labor: the model organizes and explains; sources and professionals establish what you should do.

For teams and organizations: make the boundary explicit

If employees use chatbots, publish a short policy that defines approved low-stakes tasks, prohibited data, required source checks, and mandatory human approval. Assign an owner for reviewing the policy as services and regulations change. Keep an audit trail for consequential outputs: the prompt, redacted inputs, model or service version when available, sources checked, reviewer, and final decision.

Require a named person to approve any output that affects a customer, patient, employee, legal position, financial commitment, or safety procedure. Provide a route for reporting a fabricated citation, privacy incident, discriminatory output, or unsafe recommendation. Training should emphasize that a model’s explanation is an aid to judgment, not evidence that the judgment has already been made.

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WHO’s 2025 survey of 50 responding Member States in the WHO European Region illustrates why governance matters: 86% 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 regional country-survey results, not chatbot error rates or global public-opinion figures. The findings are reported in WHO/Europe’s 19 November 2025 release.

A compact decision checklist

  • What is the consequence if this answer is wrong?
  • Am I asking for orientation, or am I asking the model to decide?
  • Have I removed identifying, health, financial, confidential, or third-party information?
  • What date, jurisdiction, edition, and facts does the answer depend on?
  • Does each important claim have a real, current, authoritative source?
  • Did I open the source and confirm that it actually supports the claim?
  • What facts would change the answer?
  • Who is the qualified human responsible for the final decision?
  • Could waiting for this chat make an urgent situation worse?

If you cannot answer the source, scope, privacy, and responsibility questions, stop at orientation and escalate. That is not a failure of the tool; it is the correct boundary for a system that can generate plausible language without dependable professional judgment.

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