Here we use a priori in somewhat loose sense, meaning data about a reaction system that, in principle, could already be available for the specific molecules and the process system, and needs to be assembled according to relevance. This relates to the concept of process context introduced by Lapkin and coworkers a decade earlier. Understanding context relevant to a specific reaction is equated to understanding of the relevant chemical and physical phenomena and their interactions. This is critical in developing the best method of parameterising the specific chemical reaction for machine learning-based reaction development workflows. In the absence of such knowledge, our models are trained on data that would likely not contain the relevant underlying phenomena and hence the models would not reach the desired accuracy and predictive ability.