Take an AI Answer Back to Verifiable Evidence

AI can help organise material and break a broad question into smaller ones. Natural prose and source-looking links, however, do not establish accuracy. First divide the answer into short claims that can be checked independently. A company’s existence, its provision of a service and a particular applicant’s eligibility are three different questions.

Rather than adding more citations, find the passage that supports each important claim. Leave any conclusion extending beyond the source’s scope unresolved. A working link and a source that supports the claim are not the same thing. Also distinguish the source’s own statement from an inference made by the model or the reader.

NIST’s Generative AI Profile discusses confidently presented false content and recommends checking sources and citations in generated output. It also cautions against generalising capability from a narrow set of successful experiences. A correct answer in one instance therefore does not establish reliable performance in another field.

The NIST AI Risk Management Framework is voluntary. Its GOVERN, MAP, MEASURE and MANAGE functions offer a structure for thinking about responsibility, context, measurement and response. Mentioning the framework does not certify a system or establish that its errors have been eliminated. The framework is used here as a source of questions, not a badge of approval.

This is an illustrative example. AI summarises the eligible applicants, fee and documents for an overseas application process. Reading the cited sources reveals that one concerns an earlier scheme and another applies to a different applicant category.

Do not adopt the whole summary unchanged. Align the category and effective period, then check the affected claims again. Correct parts need not be discarded, but unsupported numbers and conditions remain on hold. If the summary is corrected, record what changed and which source supported the correction instead of erasing the initial error from the review history.

Brainstorming possibilities and informing a contract, investment or public statement need different levels of review. For consequential uses, identify which points require original documents or specialist judgment and define where an AI answer alone cannot authorise action. Before submitting confidential or personal material, separately check the permissions and conditions of the chosen environment.

A human having read the output is not enough to describe the work as verified. Record what was checked, against which material and to what extent. Looking at a screen, opening a link, recalculating a figure and comparing the original text are different methods. Making the method visible helps the next reviewer understand what evidence can actually be reused.

When a point cannot be confirmed, record the material examined, the scope and the date. Search terms and access limits can affect what is found; absence from the results does not immediately establish non-existence. Conversely, one discovered example does not prove continuing availability. A short description of the actual search boundary helps the next reviewer continue from the remaining gap instead of repeating the same search or treating a limited observation as a universal conclusion.

AI can help structure questions, collect candidate sources and identify inconsistencies. Before making a decision, establish that a source exists, supports the relevant statement and applies to the current conditions.

An unanswered question should remain visible. Explaining why it is unresolved and identifying the next check is more useful than making a gap appear to have been filled by a confident sentence.