Hello @Dominik_Anyz, yes I suspect that it will be very difficult to achieve good results from this model without meaningful priors as there is a lot going on and it will want more regularization. You probably should work upwards from the simplest possible representation of this system to understand it fully and implement priors along the way.
If as you say there is no substantive knowledge about this system, then it may be more helpful to approach this with weakly informative priors. Presuming that the beta component of your model for example uses (link = logit), then the coefficients for this component represent effects on the logistic scale. A potential selection for weakly informative priors for categorical coefficients on that scale, presuming that very large effects are unlikely, is something like N(0,2). Given an intercept of 0 which corresponds to 0.5 on the response scale i.e. plogis(0) = 0.5, this suggests that the effect of the categorical predictor probably wouldn’t result in a transition to beyond plogis(0-4) = 0.018 or plogis(0+4) = 0.98.
It would probably be unwise to try and use the data to define prior information because this risks circular reasoning. The prior information should be justified externally to the extent possible.
A side note is that the zero-one-inflated-beta model here may be overcomplicated, unless the 0 and 1 have a concrete meaning beyond just ‘some arbitrarily extreme value’. You might be interested in the model described here (New paper using Stan/brms to estimate feeling thermometers/VAS scales).