AI hallucinations occur when an AI model generates incorrect or misleading information, presenting it as factual. This happens due to the limitations of large language models (LLMs) and large multimodal models (LMMs), which are designed to predict plausible text rather than provide accurate information. Insufficient training data, outdated data, overfitting, use of idioms or slang expressions, adversarial attacks, and poor retrieval mechanisms can all contribute to AI hallucinations. These errors can have serious consequences, including perpetuating biases, causing harm, and eroding user trust. While it is impossible to completely prevent AI hallucinations, various techniques such as retrieval augmented generation (RAG), prompt engineering, and verification can help minimize their occurrence. By understanding the causes of AI hallucinations and taking steps to mitigate them, developers can create more accurate and reliable AI tools.