Why AI Invents Facts and How to Check Its Answers

October 27, 2026

3 Minutes

The most dangerous wrong answer is often the one that reads beautifully. A precise date, a confident explanation and a reference at the bottom can make an invented claim feel finished.

That is the problem I want to examine here. Language models can produce factual errors, including statements that conflict with their source material or refer to evidence that does not exist. This behaviour is commonly called hallucination.

Confidence in the wording is not confidence in the evidence

Research on language model factuality distinguishes several problems, including gaps in encoded knowledge and failures to use knowledge reliably. There is no single explanation that makes every wrong answer easy to detect.

Asking a model to check itself can be useful as another pass, but it does not provide independent evidence. Research on mistake detection also examines how difficult it can be for models to identify their own errors.

For published work, I would therefore separate drafting from verification. A model can help produce a list of claims to inspect. The inspection still needs sources whose identity, contents and relevance you can establish.

This becomes especially important when the answer includes something very specific: an award, an equipment specification, a quotation or a release date.

A claim needs its own evidence. Extract: One exact factual claim. Find: The original source. Read: The relevant passage. Decide: Keep, qualify or remove

Check the claim, then check the source

Imagine a fictional draft that says a director won a particular festival prize in 2019. First, write that as a standalone claim. Next, look for the festival's own archive. Open the relevant year's result and check the category as well as the name.

A real page about the director is not enough. It might mention a nomination, a different award or a different year. The source has to support the exact statement you intend to publish.

For a quotation, find the original interview, recording or authorised text. If you cannot establish the wording, remove the quotation. Do not turn a paraphrase into quotation marks because it sounds memorable.

For a tool specification, check the manufacturer's documentation for the correct model and version. A review of a related model may describe a feature that yours does not have.

These are proposed editorial checks. They do not depend on whether the first draft came from a person, a model or both. The standard is the same: can a reader follow the evidence

Four source checks. Source identity, support for the claim, date and version, and remaining uncertainty.

Build a small claim ledger

For a researched article, I would keep a simple table outside the published prose. One column holds the claim, one the primary source, one the relevant passage and one the decision: keep, qualify or remove.

Qualifying a claim often improves the writing. “The documentation lists support for this format” is more precise than “this tool handles everything.” “The festival archive records a nomination” is more useful than an inflated claim about international acclaim.

Be particularly careful with comparisons. The fact that two products list the same feature does not show they deliver the same quality. A practical comparison needs a defined task, comparable settings and results you have actually observed. If you have not tested them, describe a selection method rather than inventing a winner.

Finally, check the article again after editing. A shorter sentence can accidentally lose a condition that made the original claim accurate.

I like writing that feels certain about its direction. That does not require pretending the evidence is stronger than it is. A clear boundary around what we know gives the reader something more valuable than confidence: a reason to trust the work.