A fluent AI answer can look finished before its factual claims have been checked. Names, dates and references may appear in an orderly explanation even when some details are wrong. Understanding this gap between presentation and evidence is useful for anyone using generated text to prepare a briefing, study a subject or explain an issue to other people.
A convincing answer is not a verification process
The US National Institute of Standards and Technology’s generative AI risk profile identifies confidently presented false or erroneous material as confabulation, often called hallucination. It explains that generating statistically plausible language can produce both accurate statements and factual errors. References and explanations can also be invented.
The practical consequence is straightforward: the existence of an answer is not evidence that the underlying claim has been established. A paragraph can mix a correct general description with a wrong date or an unsupported conclusion. Reviewing only its overall plausibility can leave the important detail unchecked.
Reduce a paragraph to claims you can examine
Consider a hypothetical briefing about a local transport project. An AI draft says that the project was approved, names the authority involved and gives a completion date. Those are separate claims. A useful review looks for an approval record, checks the authority’s role and establishes whether the date is a target, a contractual deadline or an actual completion.
This method changes the review question from whether the paragraph sounds sensible to what evidence supports each part. It also helps identify wording that goes beyond the source. A document may describe an option under consideration while the draft presents it as a settled decision.
A simple working note can record the claim, its source and the outcome of the check. Where a fact cannot be established, the choices include removing it, narrowing the wording or explicitly retaining uncertainty. Supplying a plausible replacement without evidence repeats the original problem.
Inspect the reference, then inspect what it supports
A citation needs two checks. First, does the cited publication exist? Second, does it support the statement attached to it? A genuine report can be used to justify a conclusion it does not actually make. The author, publication date, relevant passage and scope all matter.
The UNESCO approach to media and information literacy emphasises critical engagement with information. Applied to an AI draft, that means examining the evidence rather than treating the tool’s confident presentation as an authority. Asking for sources can help organise a search, but the sources still need to be read.
For a summary of a supplied document, compare the output with the original. Check whether qualifications, exceptions and competing interpretations survived the compression. A shorter text can remain readable while changing the meaning of an important condition.
Match the review to the consequences
Brainstorming possible headings and preparing a factual public statement are different uses. In the first, an imperfect suggestion can be discarded quickly. In the second, a mistaken claim may be repeated by readers who never see the original evidence. The review should reflect that difference.
The OECD AI Principles include transparency and accountability. A practical editorial application is to identify who approves a final text and how a correction will be made if an error is found. This is a workflow choice, not proof that a particular output is accurate.
AI hallucinations are best treated as a reason to build evidence checks into the task. The useful endpoint is not simply a polished answer, but a clear account of which claims have been verified and which remain unresolved.
