In the pursuit of making large language models (LLMs) more efficient and effective, a new hierarchical approach to training them has been developed. Titled 'The Instruction Hierarchy,' the approach aims to refine how AI prioritizes commands given by users, leading to more relevant and coherent responses. Traditionally, LLMs process instructions uniformly, without distinguishing the varying levels of importance or priority some commands might have over others. This can result in less than optimal interactions, especially when users give a series of commands, with some being more critical than others. The newly proposed instructional hierarchy involves categorizing and responding to commands based on their perceived importance. Through this method, LLMs can better understand which instructions should be addressed immediately and which can be deferred, leading to a more streamlined communication process. This development signifies a move towards creating more intuitive artificial intelligence that can adapt to the nuanced demands of human language and preference. Additionally, by effectively prioritizing user commands, these LLMs can perform tasks more efficiently, which could transform how we interact with AI in professional and personal environments.