The short answer
NIST identifies confabulation, the generation of confidently presented but erroneous or false content, as a risk associated with generative AI. It also addresses privacy, information integrity, and the role of human oversight. Before relying on an AI answer, check its source, its context, what it might be missing, who needs to review it, and what information you shared to get it.
1. Where did the answer come from?
Look for the source behind important claims. If an answer includes a citation, open it. Check whether the source exists, whether the relevant passage is present, and whether it supports the specific statement.
Treat a reference as something to inspect, not as a guarantee.
2. Does it apply to this situation?
A correct statement can still be irrelevant to the task. Check the jurisdiction, date, transaction type, audience, and assumptions. Ask whether the answer depends on information that was never provided.
If the answer moves from general guidance to a specific conclusion, examine that transition carefully.
3. What might be missing?
An answer can summarize the material it has without identifying every document or fact it lacks. Before relying on it, ask:
- Was the complete document available?
- Were relevant attachments included?
- Is there a newer version?
- Does the task require evidence outside the documents?
Missing information should influence how much weight you place on the output.
4. Who needs to review it?
Match the review process to the consequences. For a low-stakes internal draft, a basic check may be appropriate. For an output affecting contracts, compliance, finances, or other consequential decisions, identify the responsible professional before acting.
NIST's AI guidance emphasizes defining responsibilities and oversight arrangements for human–AI configurations.
5. What information are you sharing?
Before submitting business documents, understand the tool's data arrangements. Check what information is necessary, who can access it, how it is retained, and which organizational rules apply.
Privacy is part of evaluating a tool, not a separate question to consider only after adoption. NIST includes data privacy among the risks addressed in its generative-AI profile.
A useful habit: verify before acting
AI can be part of professional work without becoming the final authority. A practical routine is: inspect the source, check the context, identify missing information, and confirm who is responsible for the decision.
The purpose is not to distrust every output. It is to know what has been checked before the output becomes an action.
Further reading
The NIST Generative AI Profile is a general resource on evaluating AI risks, including confabulation, privacy, and information integrity. It is referenced here as background reading, not as a certification of any product.
Frequently asked questions
Why can't I just trust a well-written AI answer?
An answer can be well written and still be incorrect, out of context, or missing relevant information. NIST identifies confabulation, the generation of confidently presented but erroneous or false content, as a known risk of generative AI. Fluency is not the same as accuracy.
What is the most important question to ask before acting on an AI answer?
Whether the answer is suitable for the decision you are about to make. A reasonable-sounding answer for a low-stakes draft may not be reliable enough for a decision involving contracts, compliance, or finances.
Does a citation in an AI answer guarantee the claim is correct?
No. A citation should be treated as something to inspect, not a guarantee. Check whether the source exists, whether the relevant passage is present, and whether it actually supports the specific statement made.
Who should review AI-generated work before it is used?
Match the review to the consequences. A basic check may be enough for a low-stakes internal draft. For anything affecting contracts, compliance, finances, or other consequential decisions, a responsible professional should review it before it is acted on.
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