What is an AI hallucination?
An AI hallucination is an output from an AI model that sounds confident and plausible but is false, unsupported, or invented. Examples include fabricated facts, fake citations, or details that don't appear in the source material.
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Why hallucinations happen
Language models generate text by predicting likely words, not by looking facts up. When they lack information or the prompt is unclear, they can still produce a fluent answer that is wrong.
The writing style gives no clue about accuracy, which makes errors easy to miss.
How to reduce the risk
Give the model reliable source material, ask it to stay within that material, and tell it to say when it doesn't know. Techniques such as retrieval-augmented generation can ground answers in your own documents.
None of these removes the risk entirely, so check important claims, names, numbers, and links before you publish.
Why it matters for marketing
A wrong statistic, product claim, or quote can damage trust and may create legal problems. A human review step before anything goes public is the most dependable safeguard.
Common questions
Why do AI models make things up?
They generate likely-sounding text rather than retrieving verified facts. If the model lacks the right information, it may fill the gap with something plausible but incorrect.
Can you stop AI hallucinations completely?
No. You can reduce them with better prompts, trusted source material, and review, but you should assume errors can still occur.
How do you spot an AI hallucination?
Check names, numbers, quotes, and links against a reliable source. Be extra careful with specific details that you can't trace back to something real.
Does retrieval-augmented generation prevent hallucinations?
It reduces them by giving the model relevant source text to work from, but it doesn't eliminate them. The model can still misread or misstate what it retrieves.
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