AI hallucinations occur when artificial intelligence systems generate false or misleading information that appears factual. These hallucinations often stem from limitations in training data or the AI’s inability to distinguish between reality and plausible fabrication. In language models, this can manifest as confidently incorrect statements or fictional references. As AI tools become more integrated into decision-making processes, understanding and mitigating hallucinations is critical for maintaining trust and accuracy. Developers are now focusing on model transparency, validation methods, and human oversight to minimize such issues. As AI evolves, addressing hallucinations will remain a key challenge in responsible AI deployment.
