Hallucination
When an AI model generates information that is false, fabricated, or unsupported by its sources while presenting it as fact.
A hallucination is an AI-generated statement that is incorrect or invented but delivered with confidence. Models hallucinate when they lack reliable information, when sources conflict, or when they over-generalize from patterns.
Why it matters for businesses
An AI tool can hallucinate wrong details about your business — incorrect hours, prices, services, or claims — and present them as fact to a potential customer. Because the answer looks authoritative, the user may never verify it.
How to reduce hallucinations about your brand
- Make accurate facts abundant, consistent, and easy to retrieve
- Eliminate conflicting information across the web that models might average
- Use structured data to state key facts explicitly
- Monitor AI answers and correct inaccuracies at the source
Practical takeaway
You cannot stop models from hallucinating in general, but you can starve the conditions that cause them to hallucinate about you.
Related Terms
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Embeddings
Numerical representations of text that capture meaning, allowing AI systems to match a query to relevant content based on semantic similarity rather than exact keywords.
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Grounding
The practice of tying an AI model's response to verifiable external sources, reducing fabrication and allowing the answer to cite where its information came from.
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Query Fan-Out
A technique where an AI search system breaks a single user question into many related sub-queries, retrieves results for each, and synthesizes them into one answer.