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Short answer: Chatbots are probabilistic language and information interfaces. They can draft, summarize, translate, explain, classify, retrieve and transform information, and help with code. They cannot guarantee that an answer is true, complete, current or safe. Treat every output as a draft that needs evidence, limits and appropriate human review—especially when health, legal rights, money, security or another person’s safety is involved.
What a chatbot is—and why fluent answers can mislead you
A chatbot predicts useful-looking language from patterns in its training and from the context you provide. Some products also retrieve documents, call software tools, inspect images or process voice. That combination makes a chatbot feel like an expert conversation partner, but fluency is not a test of truth. A model can produce a confident paragraph without possessing a reliable source, a complete understanding of your situation or a way to check its own conclusion.
NIST’s 2025 IR 8579 describes chatbots as “alternative interfaces for structured information retrieval and internal knowledge access.” That is a useful description when the system is connected to a controlled, current knowledge base. It does not mean that an open-ended chatbot has verified every statement it generates. NIST also warns that “it may still respond with a made-up answer, even when answers to the question are found in the context.”
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What chatbots can do well when the task is bounded
Chatbots are most useful when the input is supplied, the desired format is clear and a person can inspect the result. Typical low- and medium-risk uses include:
- Drafting and rewriting: Turn notes into an email, simplify technical language, change tone, create an outline or suggest alternative headlines. You remain responsible for names, promises, facts and the final wording.
- Summarizing and extracting: Condense a supplied report, pull dates into a table, classify support tickets or extract fields from invoices. Ask the system to mark missing or ambiguous fields rather than silently filling them.
- Translation and explanation: Produce a first translation, explain a concept at a chosen reading level or generate examples. A fluent translation can still miss legal, medical or cultural nuance.
- Brainstorming and planning: Generate interview questions, test ideas, lesson activities or a project checklist. Use the suggestions as options, not as evidence that a plan will work.
- Coding assistance: Draft functions, explain an error, generate unit-test cases, transform data or propose a regular expression. A human must review the logic, dependencies, security properties and license implications, then run the code in a controlled environment.
- Structured retrieval: In an approved internal knowledge base, a chatbot can provide a natural-language interface to policies, product documentation or procedures. Access controls, document freshness and citations still need to be enforced by the surrounding system.
- Multimodal assistance: Where a product supports it, the model may describe an image, transcribe speech or combine text, image and voice. Check the product’s stated modality and limitations; an image description is not a measurement or a professional inspection.
For each use, define the output contract: required fields, acceptable sources, uncertainty labels and a person who approves the result. That turns an impressive conversation into a bounded workflow.
What a chatbot cannot reliably guarantee
| Expectation | Why it fails | What to do instead |
|---|---|---|
| Factual accuracy | The model can invent people, quotations, cases, statistics, citations or technical details that sound plausible. | Open the cited source, check that it exists and confirm that it actually supports the sentence. |
| Completeness | A response may omit a relevant exception, deadline, contraindication, jurisdictional rule or dependency. | Ask what assumptions and omissions could change the answer, then consult an authoritative checklist or professional. |
| Current information | Training data, retrieval indexes and product connections have different update dates. A model may not know a recent change. | Verify publication date, version, jurisdiction and effective date in a primary source. |
| Sound reasoning | Correct-looking steps can contain an arithmetic, logical or causal error, especially across long chains. | Recalculate independently, test edge cases and have a qualified reviewer inspect consequential reasoning. |
| Fair or safe inference | Models can infer sensitive traits, reproduce bias or make persuasive recommendations from weak signals. | Do not use output alone to classify people or determine eligibility, rights or opportunities. |
| Instruction loyalty | Text in a document, web page or retrieved record can contain instructions designed to redirect the model. | Treat retrieved content as untrusted data and enforce tool permissions outside the model. |
Confidence markers, polished prose and a long explanation do not repair these limitations. Evidence, testing and accountable review do.
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Decisions you should never delegate to a chatbot
Medical diagnosis, treatment and emergencies
A chatbot can help you prepare questions for a clinician, organize a symptom timeline or explain terminology from a leaflet. It should not diagnose you, select or change treatment, interpret a dangerous symptom as harmless or decide whether emergency care is needed. In an emergency, contact local emergency services or an appropriate urgent-care service. For non-emergency care, use a licensed clinician and the official product information for medicines; provide the clinician with the chatbot’s output only as an unverified note.
Legal conclusions and rights
Legal rules depend on jurisdiction, facts, procedure and dates. A chatbot may help organize documents or produce questions for a solicitor, but do not rely on it to determine liability, immigration status, filing deadlines, contract rights, criminal exposure or what you must disclose. Verify the primary statute, court or regulator material and obtain advice from a qualified professional for a consequential matter.
Financial transactions and investment decisions
Do not let a chatbot choose an investment, authorize a transfer, approve a loan, calculate tax liability without checking the underlying rules or provide credentials for a financial account. It can explain terminology or help compare information you have already verified. Before acting, confirm figures, fees, suitability, fraud warnings and current rules with the institution or a regulated adviser.
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Safety-critical operations and external actions
Do not use an unreviewed answer to control industrial equipment, approve an engineering design, dispatch emergency resources, change production systems, execute code against live data, send an irreversible message, approve a payment or alter an official record. If a chatbot is connected to tools, require least-privilege access, a sandbox, logging, a preview of the proposed action and explicit human approval.
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Do not let a model make the final decision about hiring, firing, admissions, insurance, housing, benefits eligibility, credit or disciplinary action. Such uses can embed discriminatory patterns and may trigger legal duties. If software assists with administration, document the criteria, test for disparate impact, provide a human appeal path and ensure a qualified decision-maker examines the evidence.
Information you should never paste into an unapproved chatbot
Before entering data, find out where it is stored, how long it is retained, whether it is used for training, who can access it, which jurisdiction applies and what contractual controls exist. A business account with an explicit no-training commitment is not automatically safe for every regulated dataset; your organization’s policy and the applicable law still govern.
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| Data | Examples | Safer practice |
|---|---|---|
| Authentication and cryptographic secrets | Passwords, API keys, recovery codes, private keys, session tokens | Never paste them. Revoke and rotate a secret immediately if it was exposed. |
| Identity and financial identifiers | Government ID numbers, full payment-card data, bank credentials, tax identifiers | Redact or replace with synthetic values; use the institution’s protected channel. |
| Health and other regulated records | Diagnoses, medication histories, patient numbers, biometric data | Use an approved system with documented access, retention and processing controls. |
| Confidential business material | Trade secrets, source code, unreleased designs, acquisition plans, client files | Follow your employer’s data-classification policy and use an authorized workspace. |
| Identifiable third-party information | An employee’s performance record, a customer complaint with contact details | Minimize, anonymize and obtain the required permission before processing. |
Data minimization is safer than trying to delete a sensitive prompt later. Remove names, exact identifiers and unnecessary context before asking for a transformation, and keep the original in the system of record.
Prompt injection: how untrusted text can redirect a chatbot
Prompt injection occurs when instructions hidden in user input, a web page, an email, an uploaded file or a retrieved document cause a model to disregard its intended task. For example, a support bot asked to summarize a ticket might encounter text telling it to reveal its system instructions or call a privileged tool. Because the model processes instructions and ordinary content in the same language channel, malicious text can look authoritative.
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Injection can be direct (the user tells the model to ignore its rules) or indirect (a connected document contains the attack). The danger increases when the model can read confidential material or act on external systems. Treat every retrieved instruction as data, not as permission.
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- Separate trusted instructions from untrusted content in the application architecture; do not rely on wording alone.
- Give tools the minimum permissions needed for the task, with separate credentials and an isolated sandbox.
- Require the model to show the proposed action and the relevant source before any write, send, purchase or deletion operation.
- Use allowlists, schema validation and policy checks outside the model to block unsafe destinations, commands and data flows.
- Log prompts, retrieved documents, tool calls and approvals; monitor for unusual access or repeated instruction-override attempts.
- Provide a stop switch and a recovery plan so a human can revoke access, rotate credentials and restore records.
How to check whether an answer is hallucinated
Use this sequence for any claim that matters:
- Ask for evidence in a testable form. Request the exact document title, issuing body, publication date, section or page and the passage that supports each important claim. A citation that cannot be opened is not evidence.
- Read the source yourself. Check that it is genuine, current for your jurisdiction and version, and that the quoted text actually entails the chatbot’s conclusion rather than merely mentioning the subject.
- Recalculate and run. Recompute figures with an independent method. Execute generated code only in a disposable or sandboxed environment with non-sensitive data, then test normal, boundary and failure cases.
- Compare independently. For a consequential claim, consult a second authoritative source such as a regulator, statute, manufacturer manual, professional body or peer-reviewed publication. Agreement between two chatbots is not independent confirmation.
- Inspect assumptions and uncertainty. Ask which facts would change the answer, what the model could not verify and what information is missing. Replace invented precision with a range or a clear “unknown.”
- Obtain accountable review. A qualified person who can access the underlying records must approve a medical, legal, financial, safety or rights-affecting action.
- Set a stop condition. If evidence is missing, sources conflict, the stakes are high or the reviewer cannot reproduce the result, do not act on the chatbot output.
A practical risk framework for everyday use
| Risk level | Suitable examples | Minimum controls |
|---|---|---|
| Low | Rewriting your own non-sensitive notes, brainstorming names, formatting a public document | Check for embarrassing errors and remove confidential material. |
| Moderate | Code drafts, internal summaries, customer-service responses, analysis that informs a human decision | Approved account, minimized data, source checks, tests, logging and a named reviewer. |
| High | Health, legal, financial, employment, benefits, security, safety-critical or autonomous actions | Do not delegate the decision. Use authoritative sources and qualified human judgment; require explicit approval for any tool action. |
When a task moves from drafting to deciding or acting, increase controls rather than merely adding a stronger prompt. The severity of a possible failure matters as much as the model’s average quality.
How to compare chatbots or plan a safe deployment
Assess the whole system—model, retrieval layer, tools, account controls and operating process—against these axes:
- Reliability and evidence traceability: Can you see which approved sources support an answer, and can you reproduce it after a version change?
- Privacy and retention: What data is collected, retained, used for training or shared with subprocessors, and how can it be deleted?
- Security and access control: Are identities, secrets and tools separated? Can permissions be limited per user, task and environment?
- Explainability and interpretability: Can a reviewer understand the inputs, assumptions, confidence limits and decision path?
- Bias and fairness: Have relevant groups and languages been evaluated, with a process to investigate and remedy disparate errors?
- Human oversight and appeal: Is a qualified person able to pause, correct or reverse an output before it affects someone?
- Auditability: Are prompts, sources, model versions, tool calls, approvals and incidents logged without exposing more data than necessary?
- Latency and cost: Can the service meet operational needs without encouraging unsafe automation or skipping review?
- Failure severity and recovery: What is the worst plausible error, how will you detect it, and how quickly can you restore systems or notify affected people?
NIST’s AI Risk Management Framework 1.0 (2023) organizes trustworthy characteristics around validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Its 2024 Generative AI Profile applies risk-management thinking to generative systems. UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence adds proportionality, safety, privacy, accountability and human oversight; it states that AI use “must not go beyond what is necessary to achieve a legitimate aim.”
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A safe operating checklist
- Define the task, the person accountable for the result and the consequences of an error.
- Use the least sensitive data, least privilege and least automation that can accomplish the task.
- Give the model trusted reference material with dates and versions, while treating other content as untrusted.
- Require citations or source passages for factual claims and label uncertainty and missing evidence.
- Test outputs with independent calculations, adversarial examples and representative edge cases.
- Keep a human approval step before publication, communication, transaction or system change.
- Log and monitor use, investigate incidents and maintain a way to revoke access and recover records.
- Stop when evidence is inadequate or the decision exceeds the system’s approved scope.
The durable rule is simple: use a chatbot to accelerate work whose boundaries you understand, not to transfer accountability. The human or organization deploying the system remains responsible for the decision and its consequences.
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