An AI hallucination is a confident, plausible-sounding answer from an AI model that is false, unsupported or made up. Examples include invented statistics, fake citations, wrong prices, or features a product doesn't actually have.
For businesses, hallucinations are not just a tech curiosity. When someone asks ChatGPT, Gemini or Perplexity about your company, the answer they get may include errors about your pricing, locations or policies, and most people won't double-check.
What is an AI hallucination?
OpenAI defines hallucinations as plausible but false statements generated by language models. The key word is plausible. A hallucination usually reads just like a correct answer, which is what makes it hard to spot.
Researchers often split hallucinations into two kinds. A widely cited survey on LLM hallucination separates factuality hallucinations, where the output conflicts with real-world facts or invents them, from faithfulness hallucinations, where the output contradicts the user's instructions or the source material it was given.
You'll also see other names. The U.S. National Institute of Standards and Technology uses "confabulation" in its generative AI risk profile, partly because some commenters felt "hallucination" makes AI sound too human.
Why AI hallucinations matter for your brand
More buying research now starts in AI assistants. If an assistant says you're closed on weekends, don't serve a city you do serve, or charge double your real price, that error shapes a decision before the person ever reaches your website.
There can be legal consequences too. In Moffatt v. Air Canada, decided in February 2024, British Columbia's Civil Resolution Tribunal held the airline responsible after its website chatbot wrongly told a customer he could apply for a bereavement fare after booking. The tribunal rejected Air Canada's argument that the chatbot was effectively a separate entity responsible for its own statements, finding the airline answers for everything on its website.
That case involved a company's own bot. Third-party assistants describing your brand are different, since you don't control them. But the lesson carries over: inaccurate AI answers about a business have real costs, and the business is the one that ends up dealing with them.
Why AI models hallucinate
They predict text, not truth
A large language model learns by predicting the next word across huge amounts of text. OpenAI's research explains that this teaches fluent language well, but rare, arbitrary facts can't be reliably predicted from patterns alone. A specific company's founding year or return policy is exactly that kind of fact.
Training rewards guessing
OpenAI also argues that common evaluation methods score models on accuracy alone, which rewards a confident guess over an honest "I don't know." Models learn that guessing pays off more often than admitting uncertainty.
Gaps and errors in the data
If your brand is barely mentioned in a model's training data, or the information out there is outdated or contradictory, the model has little to go on and may fill the gaps. The research survey cited above lists outdated knowledge and rare, long-tail facts among the main data-related causes.
Weak or misread sources
AI search tools reduce hallucinations through grounding, pulling in live web results before answering. But if the retrieved pages are wrong, unclear or conflicting, the model can still produce an inaccurate summary, or misattribute a claim to the wrong source.
An example of a brand hallucination
Imagine a family-owned bakery that stopped offering wholesale accounts two years ago. An old directory listing and a forum thread still mention wholesale. A user asks an AI assistant which local bakeries supply cafes, and the assistant confidently recommends this bakery, with a made-up minimum order size.
Nothing on the bakery's site says "we don't do wholesale," and the outdated mentions are the only signals available. The fix isn't to argue with the AI. It's to correct the sources and publish clear, current facts that grounding systems can find. This example is hypothetical, but this pattern of stale third-party data is common.
How to reduce hallucinations about your business
You can't control what an AI says, and no tactic guarantees accuracy. You can make the correct information easier to find and harder to misread.
- Test what AI says about you. Ask several assistants about your pricing, services, locations, hours and policies. Record errors and repeat monthly, since answers change.
- Publish clear, factual pages. Maintain an About page, pricing page and FAQ that state key facts plainly, in text, with dates where relevant.
- Fix the sources. Update or remove outdated directory listings, profiles and old press pages. Keep your name, address and phone consistent everywhere.
- Use structured data. Schema markup for your organization, products and FAQs helps machines understand your facts, as long as it matches visible content.
- Strengthen your entity signals. Consistent profiles and authoritative mentions make it easier for systems to connect facts to the right brand, not a similarly named one.
- Use feedback tools. Most assistants let users flag a bad answer. It's not a guaranteed fix, but it's worth doing for serious errors.
Common misconceptions about AI hallucinations
- "Citations mean the answer is correct." A cited answer can still misstate what the source says. Check the source.
- "Newer models don't hallucinate." OpenAI says its newer models hallucinate less, but the problem is not eliminated.
- "There's nothing a business can do." You can't edit an AI's answer directly, but you can improve the information it retrieves and learns from.
How Butter approaches AI hallucinations
Hallucination checks are built into how we test AI visibility. In our GEO plan, monthly AI prompt testing includes questions about your brand's facts, not just whether you're mentioned, so we can flag wrong prices, services or locations. When we find errors, our on-page and off-page recommendations focus on fixing the likely sources, and our knowledge graph submissions and schema markup guidance help give AI systems a clear, consistent picture of who you are. We can't force any AI to correct itself, and we're upfront about that. What we can do is make the accurate version easier to find.
Frequently asked questions
Can I get an AI company to correct a hallucination about my business?
There's usually no direct correction channel for businesses. Using in-product feedback helps flag errors, but the more reliable fix is improving the public information AI systems retrieve and learn from.
Does grounding stop hallucinations?
Grounding reduces them by giving the model current sources to work from, but it doesn't eliminate them. Models can still misread, mix up or overstate what retrieved pages say.
Are AI hallucinations the same as AI bias?
No. A hallucination is false or unsupported content. Bias is a skew in how a model treats topics or groups. They can overlap, for example when biased data leads to a confident but wrong claim.