In this chapter, the initial focus is on how AI models fit into a broader hierarchy, distinguishing them from foundational mathematics and algorithms. This is followed by a discussion of the key challenges in AI model development and the central role of metrics in addressing them. Next, various metrics are presented in detail, along with hyperparameter optimization methods aimed at improving model performance. The final section highlights the characteristics of a well-trained AI model, emphasizing strategies to avoid both underfitting and overfitting for robust, reliable results.