Bayesian calibration provides substantial benefits over standard approaches to calibrating building energy models. It provides an automated process for optimizing parameter values that does not rely heavily on expert knowledge. It also accounts for multiple sources of uncertainty to generate an estimate for the range of possible outcomes rather than a single model prediction. However, using Bayesian calibration can be daunting. This paper aims to make it more accessible by outlining the key steps in setting up and using Bayesian calibration and by addressing design issues at each step. Key steps include quantifying uncertainty, screening parameters, conducting model runs to generate a metamodel, and performing Bayesian calibration based on the results of the model runs and measured field data. At each step, we explore the key decisions that must be made and the tradeoffs associated with using different approaches. We also discuss and compare software packages that can be used to aid in Bayesian calibration.