A friend tells you they asked ChatGPT for the best accounting software for freelancers. It confidently recommended a tool called "Ledgerly" with a detailed breakdown of its features and pricing. One problem: Ledgerly doesn't exist. Never has. ChatGPT made it up, complete with fake details, and delivered it like established fact.
That's a hallucination.
So what exactly is an LLM hallucination?
An LLM hallucination happens when an AI model generates information that sounds correct and confident but is factually wrong. It could be an invented company name, a fabricated statistic, a misattributed quote, or a completely fictional product recommendation.
The term "hallucination" is borrowed from psychology, and it fits. The AI isn't lying (it doesn't have intent). It's pattern-matching across its training data and sometimes those patterns produce outputs that look right but aren't grounded in reality. The model doesn't "know" things the way you or I do. It predicts what word should come next, and sometimes that prediction goes off the rails.
What makes hallucinations particularly tricky is the confidence. A human might say "I think..." or "I'm not sure, but..." An LLM delivers hallucinated content with the same authoritative tone it uses for accurate information. There's no built-in uncertainty signal for the person reading the response.
Why should you care?
Hallucinations cut both ways for your brand, and neither direction is great.
On one side, an AI might recommend your competitor for something they don't actually do, or invent capabilities they don't have. A potential customer takes that recommendation at face value and you've lost a lead to fiction.
On the other side, an AI might say something wrong about your brand. Wrong pricing, wrong features, wrong positioning. A prospect who would've been a perfect fit gets turned away by information that doesn't exist.
And then there's the subtler problem: if people can't fully trust AI recommendations, every recommendation carries a shadow of doubt. Even when an AI correctly recommends your product, some users will second-guess it because they've been burned by hallucinations before.
What causes hallucinations?
Several factors contribute.
Training data gaps are the most common trigger. If the model's training data doesn't contain enough information about a topic, it fills in the blanks by extrapolating from patterns. This is why smaller or newer brands are more susceptible to hallucinated information. There's less data for the model to draw from, so it improvises.
Ambiguity in the prompt plays a role too. Vague or broad questions give the model more room to generate plausible-sounding but inaccurate responses. Specific questions anchored to verifiable facts tend to produce more reliable output.
Model architecture matters as well. Language models are fundamentally prediction engines. They're optimized to produce fluent, coherent text, not to verify factual accuracy. Accuracy is a secondary outcome, not a primary goal.
How RAG helps reduce the problem
Retrieval-Augmented Generation (RAG) is one of the main approaches to fighting hallucinations. Instead of relying solely on training data, RAG-equipped systems first retrieve relevant documents from a trusted source, then generate answers based on that retrieved information.
SearchGPT and Perplexity both use forms of RAG. When they search the web before answering, they're grounding their response in real, current sources rather than relying purely on what the model "remembers." This dramatically reduces (but doesn't eliminate) hallucination rates.
This is also why having strong, clear, well-structured content on your website matters. When AI systems use RAG, your content becomes the grounding material. If your information is accurate, detailed, and easy to extract, the AI is more likely to represent you correctly.
The honest truth
Hallucinations are getting less frequent as models improve, but they're not going away. Every major AI provider is working on the problem, but the fundamental architecture of language models makes zero hallucinations an unlikely near-term outcome.
For your brand, this means monitoring what AI says about you isn't paranoia. It's practical. You can't fix misrepresentations you don't know about.
