You should put priors on the parameter values of a model, not its truth/falsehood. I have no idea where the latter idea comes from. There may be something to it

You should put priors on the parameter values of a model, not its truth/falsehood. I have no idea where the latter idea comes from. There may be something to it but it sounds nonsensical to me. For example, a model of carcinogenesis could use a prior for mutations rates centered on around 10^-6 somatic mutations/base-pair/cell division rather than 10^-100 or 10^100. The first is too rare and second too common to be consistent with other lines of evidence. The priors don't have to be exact, just in the right ballpark to constrain the model output to realistic scenarios. Of course you could argue about what's a realistic mutation rate, just like you could argue about the choice of model itself (maybe some people believe somatic mutations are a result rather than cause of cancer, so including those values in the model is wrong to begin with).