What are AI Hallucinations?

This post defines AI hallucinations as a phenomenon where AI models generate plausible but factually incorrect or nonsensical information. It discusses the underlying causes, such as limitations in training data, model architecture, and the probabilistic nature of language generation. The post also touches upon strategies for mitigating hallucinations, including improved data curation, advanced model training techniques, and incorporating external knowledge sources. While not detailing a specific system build, it provides crucial context for understanding and addressing a core challenge in AI development and deployment.