I am learning MCMC for the purpose of doing Bayesian inference. In Andrieu, 2003, it is mentioned that:
... in order to obtain the best results out of this class of algorithms, it is important that we do not treat them as black boxes, but instead try to incorporate as much domain specific knowledge as possible into their design.
The article was written 15 years ago. I am aware that now we have packages like Stan and JAGS (in R) that relieve users from worry about how to set the parameters of various MCMC algorithms to achieve best mixing.
My question is: Is domain knowledge still relevant when doing Bayesian inference using MCMC? (Edit: Do we still need to use domain knowledge to tweak the various algorithms, like choosing the proposal distribution, or setting the tuning parameters?)
If your answer is yes, could you give one example of where domain knowledge had helped you achieving better results? The more specific, the better.