AI hallucinations are fabricated or incorrect responses produced by AI models. Discover how to detect them and improve accuracy with stronger safeguards.
by Databricks Staff
AI hallucinations are outputs that sound coherent and confident but are factually wrong, fabricated, or unsupported by the AI model's training data. It happens across chatbots, image generators, and multimodal systems: a chatbot might invent a legal citation that doesn't exist, or an image model might add an extra finger to a hand. The model isn't perceiving anything; it's predicting the next likely word, and sometimes that prediction is a plausible-sounding falsehood.
Hallucinations aren't rare edge cases. They're a built-in property of how these models work, and they create real risk for anyone deploying AI, from legal liability and regulatory compliance to customer trust.
Hallucinations aren't bugs. They come from how generative AI models are built and trained. A few main factors are behind them:
