The Design or Build stage of an AI project encompasses data preparation, model development, training, and testing. This phase is fundamentally iterative, requiring repeated cycles to enhance model accuracy. Engineers and data scientists experiment with various algorithms, optimize parameters, and verify outcomes. It follows the initial problem definition stage and lacks a fixed duration, as its length is contingent on project complexity. Furthermore, this stage extends beyond data acquisition to include feature engineering, model training, and validation. Therefore, the accurate characterization of the Design or Build phase is that it is an iterative process.