What's the consquences of training a KNN model over the whole data? I know it is a basic question, but is the only consequence not having a good evaluation of the data?
is the accuracy or precision of the model affected in any way if there is no test set? worse performance maybe?
I know that the results would be kinda meaningless cause we have no way of testing the model, the model is trained on the whole data, but is there something beyond?

The consequence is the same as with training any other algorithm on all the data: you have no way of out-of-sample testing the model and validating if it doesn’t overfit. With $k$NN you could set $k=1$ and get a model that perfectly fits the training data, but you have no idea how it will work out on external data. This shows how in-sample metrics can be meaningless. If you are perfectly sure that the distribution of the prediction-time data is exactly like your training data, you could take the bet, but usually it’s unlikely the case that you can be sure about that.
