How Adaboost calculates error for each weak learner in training?
When I started learning about ensemble methods, this youtube video help me greatly.
The idea is that you fit the model on the data, calculate the error and then work with the error. So yes you should use a weak learner to predict your data and then, use another weak learner.
There are some more videos in the same channel that give a great explanation to have an intuition.