Verify AI answers before you act.
A practical check for separating an AI-generated draft from evidence, verifying sources, and assigning responsibility before using an answer at work.
Website editorial guide · · 3 min read
Prepared with AI assistance for this website. Practical exercises apply the Capability framework; they are not Neil’s personal accounts or research findings. External evidence is identified where used.
An answer can be clear, detailed, and wrong. NIST’s Generative AI Profile identifies confabulation: generated content presented confidently even when it is false. It also recommends checking sources and citations. Fluency gives you something to read; verification gives you grounds to act.
The workflow below is an editorial checklist for ordinary work. It is not a certification of an AI system or a replacement for your organization’s review process. Use it to turn a plausible draft into a set of claims someone can examine.
Name the decision before reviewing the answer
What will change if you use this output? A brainstorm for a meeting has different consequences from instructions that alter a production system. State the intended use, the people affected, and the cost of an error. That tells you how much independent checking the task needs.
Do not paste confidential, personal, or customer information into a service until you have confirmed that the use is allowed by your organization and the service’s applicable terms. A tool available to you is not necessarily approved for every kind of information.
Pull out the claims that matter
Separate facts, assumptions, recommendations, and unknowns. A draft may mix them into a single smooth paragraph. Rewrite the decisive statements as a short list. For each, ask what would make it true and where you can check it.
For an illustrative vendor comparison, decisive claims might include whether a feature exists on the plan you would buy, whether it supports your required region, and what happens to uploaded data. A general description of the vendor is not enough to support those decisions.
Ask the model to identify uncertainties and cite primary sources, but verify those sources yourself. Asking for citations is a request for more output, not proof that the output is grounded.
Open the original source
Follow the link. Confirm that the page exists, that it belongs to the relevant publisher, and that its text supports the specific claim. Check the date and whether the information applies to the exact product, version, region, or situation you are evaluating.
Prefer the source closest to the claim: official product documentation for a feature, the actual research paper for a research finding, or your own approved operating procedure for a company process. A source can be authentic and still be outdated or irrelevant.
Keep a short record: the claim, the source, when you checked it, and any limit that changes its use. If you cannot verify a claim, mark it unresolved rather than polishing it into a confident sentence.
Test the proposed use and name the owner
If the output is code or a procedure, review it before trying it in a controlled environment appropriate to the task. Check ordinary cases, a failure case, and how you would recover. One successful example does not demonstrate reliability across every situation.
Name the person authorized to accept the result. Record which parts were checked, which remain uncertain, and what should trigger escalation. “The AI said so” does not tell a colleague who owns the decision.
If the task requires legal, medical, financial, or other specialist judgment, use the qualified reviewer your situation requires. The checklist helps organize evidence; it does not supply the expertise or authority to approve every decision.
A five-line verification record
- Decision: what will this answer be used to do?
- Claim: which statement is essential to that decision?
- Evidence: which original source supports it, and when did you check?
- Limit: what remains unknown or needs testing?
- Owner: who can approve the use and respond if it fails?
Sources and scope
- NIST AI 600-1: Generative Artificial Intelligence Profile
Confabulation is discussed in section 2.2; source and citation verification appears in action MS-2.5-003. The checklist in this guide is our practical adaptation, not a NIST-approved process.
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