# AI Hallucination

> Source: https://rankxai.com/glossary/ai-hallucination · Last updated: 2026-08-18

An AI hallucination is a statement a language model produces confidently and fluently that is not true. AI hallucinations are not malfunctions in the usual sense: the same mechanism that generates a correct sentence generates an invented one, and the output carries no signal distinguishing the two.

## Why does the mechanism produce them at all?

Because a language model is producing plausible continuations rather than looking anything up. When the training data contained the answer, the plausible continuation is usually the true one. When it did not, the model still produces a plausible continuation, and plausibility is exactly what makes a wrong answer hard to spot: it has the shape, the register and the confidence of a right one.

This is why hallucinations cluster where they do. Specific, rare, checkable details are the most likely to be invented and the least likely to be questioned: a citation, a URL, a company’s founding year, a version number, a person’s job title. It also explains why hallucinations about small brands are more common than about large ones.

There was less in training about you, so there is less to recall and more to compose, and the composed version is assembled from what is typical of companies like yours rather than from anything about you. That is the uncomfortable version of the AI visibility argument, and it is more honest than the usual one: the fix for being described wrongly is often the same as the fix for being described rarely.

## How much does grounding fix?

Some, and less than the marketing implies. [Grounding](/glossary/grounding) ties spans of an answer to retrieved sources, and Google describes it as letting a model “cite verifiable sources beyond its knowledge cutoff”. That constrains invention, and it does not verify anything: a claim grounded perfectly in a page that is wrong, out of date or somebody’s marketing copy arrives looking exactly as reliable as a good one.

For a brand this cuts in an uncomfortable direction. Grounding raises the value of your accurate pages and raises the cost of your inaccurate ones, because a stale page of yours is now a source with your name attached rather than a page nobody read.

## What should you actually do about hallucinations concerning your brand?

Fix the retrievable record first, because it is the only input you control. Most brand hallucinations are not invention from nothing: they are an old fact still sitting on a page somewhere, a claim on a third-party listing you have not updated, or an absence the model filled. A current, plainly stated, well-structured page is the cheapest correction available.

Then accept a limit. There is no correction channel into a model’s memory, no takedown process for a wrong sentence, and no way to confirm a fix has landed except by asking again over time. Anybody offering to remove a hallucination on request is selling something that does not exist.

## Sources

- [Google: Grounding with Google Search, Gemini API documentation](https://ai.google.dev/gemini-api/docs/grounding), checked 2026-08-18
- [Anthropic: context windows, on training corpus versus working memory](https://platform.claude.com/docs/en/build-with-claude/context-windows), checked 2026-08-18