In AI, a hallucination is output that sounds confident and fluent but is wrong or has no basis in the model's sources. Language models generate likely-sounding text, and when they lack accurate information they can fill the gap with something plausible. For brands, hallucinations show up as incorrect pricing, features that do not exist, outdated positioning, made-up integrations or citations to pages that say something else.
Why models hallucinate
A model is trained to produce plausible continuations of text, not to check facts. If its training data was thin or outdated on a subject, or if retrieved sources are ambiguous or contradictory, it may produce an answer that fits the pattern of a correct answer without being one. Retrieval and grounding reduce hallucination but do not remove it: a model can still misread a source or merge details from two sources.
- Sparse or outdated information about the subject
- Conflicting sources across the web
- Ambiguous names shared with other products or companies
- Questions that assume a false premise
What brands can check and fix
You cannot edit a model directly, but you can improve the information it draws on. Make the facts on your own site clear, current and easy to extract: plan names, prices, limits, integrations and supported regions. Correct outdated descriptions on third-party pages you can influence, such as directories, partner listings and review profiles. Then recheck answers over time, because results change as models and indexes update.
Where a vendor offers a feedback or correction mechanism, use it for serious errors, but do not rely on it as the main fix.
Example
A free plan that does not exist
Pinemarsh, a fictional project tool, retired its free plan last year. Assistants keep telling users it has one. A review finds the old plan still described on two comparison sites and on an outdated Pinemarsh help article. Updating the help article, asking the comparison sites to correct their pages and adding a clear 'Plans and pricing' summary to the pricing page gives retrieval accurate sources to use.
Common mistakes
Only checking once
Answers vary between runs, products and model versions. Track key prompts over time.
Blaming the model and stopping there
Many brand hallucinations trace back to stale or contradictory information on the web, including your own old pages.
Questions
It reduces them by giving the model sources, but models can still misread or misattribute information. Citations make errors easier to check, not impossible.
Most AI products have a feedback option on answers. Whether and how that feedback is used is not usually documented, so fixing the underlying sources is the more reliable route.
