You have to be careful about that. Aleks and Andrew wound up recommending a Cauchy prior which Andrew has largely disavowed. And it really depended on standardizing the regression predictors. Otherwise, the regression coefficients completely depend on the problem. For instance, if I give you distances in angstroms or in light years, the regression coefficients change scale—so you have to standardize everything (convert covariates to be centered with unit scale, i.e., standardized). Even better, apply a QR decomposition and work with the orthogonal, unit-scaled Q matrix.
Also, you’re not dealing with “most regressions”, you have a specific regression about which you probably know at least a little bit or you won’t be able to assess whether your model’s working for whatever purpose it’s intended.
I’d recommend something like normal(0, 5) if you expect the regression coefficients to be on the order of 5 or smaller.