Cropping system models are deployed as valuable tools for informing agronomic decisions and advancing research. To meet this demand, early career scientists are increasingly tasked with building crop models to fit into these system modelling frameworks. Most, however, receive little to no guidance as to how to do this well. This paper is an introduction to building a crop model with a focus on how to avoid pitfalls, minimize uncertainty, and maximize value. We synthesized knowledge from experienced model builders and literature on various approaches to model building. We describe (1) what to look for in a model-building dataset, (2) how to overcome gaps in the dataset, (3) different approaches to fitting and testing the model, and (4) how to avoid common mistakes such as over-parameterization and over-fitting the model. The process behind building a crop model can be overwhelming, especially for a beginner, and so we propose a three-pronged approach: conceptualize the model, simplify the process, and fit the model for a purpose. We revisit these three macrothemes throughout the paper to instil the new model builder with the methodical mindset needed to maximize the performance and impact of their crop model.