The article discusses how to design experiments to predict outcomes in linear mixed models with both fixed and random effects. New design criteria are suggested to find the best designs for accurate predictions, especially when the variance components are unknown. Taking into account the uncertainty in estimating these components is crucial for optimal designs. The research emphasizes the importance of considering this uncertainty in obtaining accurate predictions for individual curves or future observations.