Note that the table as-is needs a little work since it’s still not clear from the table what is included or excluded in the predictions. This is why the response to the table noted in that same thread focuses on describing treatment of random effects first, then thinks about substantive use cases Confusion on difference between posterior_epred() and posterior_predict() in a mixed effects modelling context - #2 by bwiernik
The table in “which average is best?” at this post also discusses this A guide to correctly calculating posterior predictions and average marginal effects with multilievel Bayesian models | Andrew Heiss
I do think that providing the model predictions are drawn from in the documentation is probably the best way to remove ambiguity.