What is AI Hallucinations
Errors in which the AI confidently produces fictitious, inaccurate or unconfirmed information.
Definition
AI hallucinations are most often discussed in language models, but similar errors occur in other systems. The model doesn't know the facts in the human sense: it constructs a likely answer based on the data, the context, and the query. Therefore, she can invent a source, incorrectly paraphrase a document, mix up facts, or confidently answer where uncertainty needs to be acknowledged.
Example
The user asks for a list of scientific articles, and the model creates plausible titles and authors that actually do not exist.
Why it matters
The term is important for anyone using AI in their work: the answers cannot be automatically assumed to be the truth, especially in medicine, law, finance and publishing.
How it works
Hallucinations are reduced by checking sources, document searching, restrictions, testing, citing, manual validation and tuning the model to the task.
Where it is used
- checking chatbot responses
- working with documents
- search for facts and sources
Limitations
It is difficult to completely eliminate hallucinations. Even a strong model can make mistakes if the query is vague, there is little data, or the context is inconsistent.