An AI hallucination is a statement produced by a language model that sounds confident but is false or unsupported. Grounding is the practice of tying a model’s answer to retrieved sources so that it can be checked. Grounding reduces hallucinations but does not remove them, which means AI assistants can describe a company wrongly even while citing that company’s own website.
Why models hallucinate
A language model generates text by predicting likely next words. It has no built-in sense of whether a statement is true. When its training data or retrieved sources do not contain the answer, it can still produce a fluent answer that fits the pattern of the question. Common causes include:
- The question asks about something the model has little or no information on, such as a small or new company
- Two similar entities are merged, for example two companies with similar names
- An outdated source is retrieved, such as an old directory listing
- The model fills a gap with a plausible detail, such as a founding year or a service the company does not offer
How well does grounding work?
Better than no grounding, but not reliably. A 2023 Stanford study, “Evaluating Verifiability in Generative Search Engines” by Nelson F. Liu, Tianyi Zhang and Percy Liang, audited four generative search engines. On average only 51.5% of generated sentences were fully supported by their citations, and 74.5% of citations supported the sentence they were attached to. The systems have improved since, but the finding explains a pattern businesses still see: a correct link next to an incorrect sentence.
What hallucinations look like for a business
| Type | Example | Likely source |
|---|---|---|
| Wrong service | “The company also offers paid advertising management” | An old page, a directory category, or a similarly named company |
| Wrong location | An office city the company left years ago | Outdated listings or press releases |
| Wrong people | A former employee described as a founder | Old LinkedIn data or an old About page |
| Invented facts | A founding year or client name that appears nowhere | Gap filling where no source exists |
| Competitor mix-up | Your product described with a competitor’s features | Comparison pages that list both without clear separation |
How to find and fix them
- Write a set of questions about your company: what it does, where it is, who runs it, what it costs, who it suits.
- Ask each major assistant several times, since answers vary between runs.
- Record every incorrect statement and the sources cited next to it.
- Trace each error. If a source is cited, check it. If none is, search for the wrong fact to find where it appears on the web.
- Correct what you control: your own pages, profiles and listings. Ask third-party sites to update what you do not control.
- Make the correct facts easy to find: a clear About page, consistent profiles, Organization and Person schema.
- Use the feedback buttons in ChatGPT, Gemini and Google’s AI Overviews to report specific wrong answers.
- Re-check after the relevant crawlers have had time to revisit the corrected pages.
Errors that come from a model’s training data, rather than from live retrieval, change only when the model is retrained. Correct sources on the web are still the only way to influence that.
If you run your own AI assistant
Businesses that put a chatbot on their own site carry the risk of its answers. In a 2024 case, a Canadian tribunal held Air Canada responsible for incorrect refund information its website chatbot gave a customer. A chatbot for your own site should answer only from your approved documents, show the source for each answer, and say so when it does not know.
Frequently asked questions
Can I stop AI assistants from saying wrong things about my company?
Not fully. You can reduce errors by fixing the sources they draw on and by keeping facts consistent, and you can report wrong answers. Nobody outside the AI companies controls the output.
Does being cited mean the answer is accurate?
No. A citation shows which source the system used. Whether the sentence matches that source has to be checked by reading the cited page.