During which phase of the generative AI model lifecycle are tests run to measure the model's accuracy?

The Evaluation phase is where the generative AI model's performance, including its accuracy, is systematically measured using various metrics and test datasets. This phase assesses how well the model generalizes to new, unseen data and meets predefined performance criteria. Deployment is incorrect because it's the stage where the model is put into production for real-world use, occurring after evaluation. Data selection is incorrect as it involves choosing and preparing the data for training, which happens much earlier in the lifecycle. Fine-tuning is incorrect because it's a process of further training a pre-trained model on a smaller, specific dataset to improve its performance for a particular task, which may involve internal testing but the primary, comprehensive accuracy assessment occurs in the dedicated Evaluation phase.