Seetting a prior on the outcome

I have a multivariate hierarchical model with very few observations (2 per cluster). Consequently, the model takes hours to be fitted, with a lot of divergent transitions and low n.eff. ratios in the coefficients.
But my biggest problem is that it produces predictions that are totally out of scale and impossible (my focus is prediction).
I’m trying to fiddle with priors to fix this, but, is there a way to put priors on the outcome possible values and let all the rest of the sampling be dictated by this? that is, discard features’ values that allow such extreme values?

Thank you

The “prior” on the outcome is commonly known as likelihood and can be specified via the family argument of brms.

In addition to what Paul said, you could do a prior predictive simulation. Fit a model with just the priors(brms does it when you use the argument sample_prior = "only". Then you simulate your outcome from this joint prior distribution of your parameters. Either use the tidybayes package or generate new predictions on new synthetic data.