Evaluating an AI model’s performance is crucial to determine how well it will perform in real-world scenarios. The evaluation helps assess the model’s accuracy, generalization, and robustness, ensuring it will work effectively on new, unseen data. This evaluation process involves measuring performance metrics such as accuracy, precision, recall, and F1-score, helping decide if the model is ready for deployment or needs further refinement.