AI hallucinations, where models generate factually incorrect or nonsensical information, pose a significant challenge to the reliability of artificial intelligence. This article delves into the underlying causes, from data quality and model architecture to inference-time factors. We then explore effective mitigation strategies, including advanced data curation, sophisticated training techniques, robust prompt engineering, and the power of Retrieval-Augmented Generation (RAG). Learn how to build more trustworthy and accurate AI systems.