The article discusses creating rules to understand Kolmogorov complexity by defining its basic properties. By analyzing different types of complexity, like conditional and prefix-free complexity, researchers aimed to establish clear guidelines for measuring how much information is needed to describe something. They found that the rules developed for plain complexity by Shen also apply to conditional complexity. However, when it comes to prefix-free complexity, the existing rules don't work well. This study advances the understanding of how to quantify the amount of information needed to represent data and highlights the complexities in defining these rules for different types of information.