Why AI Makes Up Answers and How to Check Them
An AI answer can sound certain while naming a nonexistent study, inventing a product feature or confusing two dates. Treat factual output as a set of claims to inspect. The aim is to build a quick checking habit that matches the consequences of being wrong.
Why it matters
Verification becomes easier when an answer is broken into individual statements. A paragraph containing a date, a price and an eligibility rule contains three separate things to confirm.
A practical workflow
- Ask for a concise answer that distinguishes supplied facts, outside information and uncertain conclusions.
- Extract the names, numbers, dates and quotations that would matter if they were incorrect.
- Open the original source for each consequential claim and check that the source actually supports it.
- Remove unsupported details or mark uncertainty plainly instead of preserving them for a smoother narrative.
An example
Suppose a tool comparison claims a free plan includes commercial rights. Find the provider's current licensing page and check the relevant plan. A pricing screenshot alone may not establish how generated outputs can be used or whether restrictions apply.
A mistake to avoid
Asking the same assistant whether it is sure is not independent confirmation. It may repeat its earlier error. A working link is also insufficient if the linked page discusses a different product or period.
Try it and check the result
Review a short AI-generated comparison and label each factual statement confirmed, contradicted or unresolved. A useful final version should make those unresolved details visible instead of hiding them inside confident prose.
Reference and further reading
NIST AI Risk Management Framework.
The workflows and examples in this guide are illustrative. Product controls and availability can change; check the linked official guidance for your setup.
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