In artificial intelligence, a hallucination is output from a model that is presented as fact but is false, unsupported, or invented. A large language model might cite a study that does not exist, quote a price your business has never charged, attribute a service to you that you do not offer, or state an incorrect date with complete confidence. The term covers everything from small factual slips to entirely fabricated sources.
Hallucinations matter to businesses in two ways. First, AI assistants such as ChatGPT, Gemini, Perplexity, and Google’s AI Overviews may say inaccurate things about your company to people researching it. Second, if you deploy an AI tool yourself, such as a website chatbot, its mistakes are your mistakes in the eyes of your customers.
Why language models hallucinate
A language model does not look facts up in a database. It generates text by predicting, word by word, what is most likely to come next given everything it learned in training and everything in the current conversation. Most of the time, the most likely continuation is also correct. When the model lacks reliable information, the most plausible-sounding continuation can be wrong, and nothing in the basic process forces the model to say “I do not know” instead.
Several conditions make hallucinations more likely.
Gaps in training data. Small businesses, niche topics, recent events, and local details are thinly represented, so the model fills in with patterns from similar entities.
Outdated information. Training data has a cutoff. A model may describe your services, staff, or prices as they were years ago.
Conflicting sources. When different websites say different things about a business, the model may blend them or pick the wrong one.
Ambiguity. Two businesses with similar names, or a name that is also a common phrase, can be confused with each other.
Pressure to answer. Questions that assume a specific answer exists (“What does this company charge per hour?”) encourage the model to produce one.
Long or complex tasks. In multi-step reasoning or long documents, early errors can propagate, and details can be lost or distorted.
Types of hallucination
Factual errors: wrong dates, figures, names, addresses, or prices.
Fabricated sources: citations, quotes, statistics, or URLs that do not exist.
Entity confusion: merging two people or businesses, or attributing one company’s reviews, services, or history to another.
Unsupported claims: statements that go beyond what the source material says, such as inventing a guarantee or policy.
Misread sources: summarizing a real page incorrectly, reversing its meaning, or missing a key qualification.
When AI assistants hallucinate about your business
As more people ask AI assistants for recommendations and background on companies, what those assistants say about you becomes part of your reputation. Common problems we see include outdated service lists, wrong locations or phone numbers, pricing that was never published, confusion with a similarly named company, and descriptions so generic they could apply to any competitor.
You cannot edit an AI model directly, but you can change the information it draws on. The most effective steps are:
Publish clear, specific facts on your own site. Say plainly what you do, who you serve, where you are, and how engagements work. Answer the questions people actually ask in direct language. Vague marketing copy gives models nothing concrete to repeat.
Make your identity unambiguous. Consistent name, address, phone, and description everywhere, with complete structured data. This is the core of entity SEO.
Correct the sources. Fix outdated directory listings, profiles, and third-party pages, since models and AI search tools often draw on them.
Earn corroboration. AI systems give more weight to facts confirmed by several independent sources. Reviews, press coverage, and client case studies all help. We explain this dynamic in Winning the consensus layer.
Check regularly. Ask the major assistants about your business and your category and record what they say. Our AI model testing prompt template gives a repeatable way to do it, and our AI visibility plans track it monthly.
AI search tools that browse the web before answering, such as Perplexity and Google’s AI Overviews, tend to reflect corrections faster than answers drawn from a model’s training alone, because they retrieve current pages.
When your own AI tools hallucinate
If you put a chatbot on your website or use AI to draft customer communications, its errors carry real consequences. In a widely reported 2024 case, a Canadian tribunal held Air Canada responsible after its website chatbot gave a customer incorrect information about bereavement fares; the airline’s argument that the chatbot was responsible for its own statements was rejected. The principle generalizes: a business is accountable for what its AI tools tell customers.
The main ways to reduce hallucinations in your own tools are:
Ground answers in your own content. Retrieval-augmented generation has the model answer from your actual documents, policies, and product data, retrieved at the time of the question, rather than from general knowledge.
Instruct the model to stay within its sources. A well-written system prompt tells the assistant to answer only from provided information, to say when it does not know, and to hand off to a person for anything outside its scope.
Keep the knowledge base current. An assistant grounded in last year’s price list will confidently quote last year’s prices.
Limit scope. A narrow assistant that answers questions about your services and hours is far more reliable than a general one that will attempt anything.
Show sources. Linking answers to the page they came from lets customers verify and makes errors easier to spot.
Review high-stakes output. Anything involving money, legal commitments, medical information, or policy exceptions should be confirmed by a person.
Test and monitor. Try the questions customers actually ask, including awkward ones, before launch, and review conversation logs regularly afterward.
Hallucination in AI-generated content
The same problem applies to content written with AI assistance. Drafts can include invented statistics, misattributed quotes, and plausible but wrong technical details. Every factual claim in AI-assisted content should be checked against a primary source before publishing. Beyond the reputational risk, content filled with unverified or inaccurate claims undermines the trust signals search engines look for, and Google’s spam policies target content mass-produced with little human oversight. AI can speed up writing; it does not replace fact-checking.
Hallucination, outdated information, and bias
Not every wrong AI answer is a hallucination in the strict sense. An answer that was true two years ago and is no longer true is outdated information: the model is faithfully repeating an old source. An answer that consistently favors certain businesses, regions, or viewpoints reflects bias in the underlying data. And an answer that accurately summarizes a wrong web page is a retrieval of bad information rather than an invention. The distinction matters because the fixes differ. Outdated answers are fixed by updating and corroborating current information; bias is addressed by broadening the sources that describe you; bad sources are fixed at the source. True fabrications, such as invented citations, are best countered by grounding and verification.
Will hallucinations go away?
Models have become substantially more reliable, and techniques such as retrieval, tool use, and better training have reduced hallucination rates considerably for many tasks. But because language models generate rather than look up, some level of error remains inherent, particularly on obscure topics and details not well represented in their sources. The practical stance for a business is to assume errors will occur, design systems that catch them, and make the accurate information about your business as clear and widely corroborated as possible.
Getting help
If AI assistants are describing your business inaccurately, or you want a website assistant that answers from your own information and knows when to hand off, our AI chatbot development and AI visibility services address both sides of the problem. Book a discovery call to talk it through.
