Hallucinations are errors made by artificial intelligence models when they generate false or inaccurate information but present it with high confidence as if it were true. It's like the
AI model "making up" information to complete its responses.
This phenomenon occurs when an
AI model produces content that appears coherent and well-structured but lacks a factual basis or contains inaccuracies. For example, it might fabricate dates, events, or details that never happened or create false quotes attributed to real people.
Hallucinations are especially problematic in applications requiring accuracy, such as medical report generation or technical documentation. Techniques like
RAG (Retrieval-Augmented Generation) have been developed to anchor the model’s responses to verifiable sources of information.
A common case occurs when you ask an AI assistant for information about recent or specific events outside its
training. Instead of admitting it doesn't know, it might generate a response combining real facts with fabricated information.