Suppose I want to find a linear model with Gaussian error for a given data set. (The data set contains insurance claims and the end goal is to predict claim cost from claim features.) Also, suppose that I use some sort of stepwise model selection method, let's say based on minimizing AIC, to select the variables to include in the model. The main focus is on predictions and interpretation of the coefficients would be a plus.
My question is: when do I check that the model assumptions are valid? before/after or at each step of the stepwise procedure?