Prior are now not subjective but useful, the OP is about the problem of choosing the best priors. The best options are informative priors (1) and regularizers (2). So, for example, choosing as prior a Laplace distribution for the unknown parameters is equivalent to the LASSO that is a well known way of obtaining sparse models with few coefficients. In (2) there is an example in which a prior suggest a useful regularization method for regression. In (3) the author discusses prior modeling.
(1) https://en.wikipedia.org/wiki/Prior_probability#Informative_...
(2) https://skeptric.com/prior-regularise/index.html
(3) https://betanalpha.github.io/assets/case_studies/prior_model...