The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials. LLMs can be instructed with text prompts to generate comprehensive text descriptions of code snippets. However, the lack of transparency in the end-to-end generation process has hindered the understanding of model behavior and limited user control over the generated results. To tackle this challenge, we introduce a novel approach that breaks down the programming tutorial creation task into actionable steps. By employing the tree-of-thought method, LLMs engage in an exploratory process to generate diverse and faithful programming tutorials. We then present SPROUT, an authoring tool equipped with a series of interactive visualizations that empower users to have greater control and understanding of the programming tutorial creation process. A formal user study demonstrated the effectiveness of SPROUT, showing that our tool assists users to actively participate in the programming tutorial creation process, leading to more reliable and customizable results. By providing users with greater control and understanding, SPROUT enhances the user experience and improves the overall quality of programming tutorial. A free copy of this paper and all supplemental materials are available at https://osf.io/uez2t/?view_only=5102e958802341daa414707646428f86.

翻译：大型语言模型（LLMs），如ChatGPT的快速发展，彻底改变了编程教程创建的效率。通过文本提示，LLMs可被指示生成代码片段的详尽文字描述。然而，端到端生成过程中缺乏透明度，这阻碍了对模型行为的理解，并限制了用户对生成结果的控制。为应对这一挑战，我们提出了一种新颖的方法，将编程教程创建任务分解为可操作的步骤。通过采用思维树方法，LLMs进行探索性过程，以生成多样且可靠的编程教程。随后，我们介绍了SPROUT，一款配备一系列交互式可视化的创作工具，使用户能够更好地控制和理解编程教程的创建过程。一项正式的用户研究证明了SPROUT的有效性，表明我们的工具能帮助用户积极参与编程教程的创建过程，从而获得更可靠和可定制的结果。通过赋予用户更强的控制力和理解力，SPROUT提升了用户体验，并改进了编程教程的整体质量。本文及所有补充材料可免费获取，地址为https://osf.io/uez2t/?view_only=5102e958802341daa414707646428f86。