标题： 理解提示编程任务和问题 显示英文标题

标题： Understanding Prompt Programming Tasks and Questions

摘要： 通过提示引导基础模型（FMs），如大型语言模型（LLMs），已经实现了以前只能通过微调FMs才能实现的新的AI驱动软件功能（例如，文本摘要）。现在，开发人员正在将提示嵌入到软件中，这被称为提示程序。提示编程的过程要求开发人员对其提示进行许多更改。然而，开发人员用来更新其提示的问题尚不清楚，尽管这些问题的答案会影响开发人员计划其更改的方式。随着越来越多的研究和商业提示编程工具的出现，尚不清楚提示程序员的需求是否得到了充分满足。我们通过开发一个包含25个提示程序员执行的任务和51个他们提出的问题的分类法来解决这些挑战，并衡量每个任务和问题的重要性。我们采访了16位提示程序员，观察了8位开发人员进行提示更改，并对50位开发人员进行了调查。然后，我们将该分类法与48个研究和商业工具进行了比较。我们发现，提示编程的支持并不完善：所有任务都是手动完成的，51个问题中有16个——包括大部分最重要的问题——仍未得到解答。基于此，我们概述了提示编程工具的重要机遇。 显示英文摘要

摘要： Prompting foundation models (FMs) like large language models (LLMs) have enabled new AI-powered software features (e.g., text summarization) that previously were only possible by fine-tuning FMs. Now, developers are embedding prompts in software, known as prompt programs. The process of prompt programming requires the developer to make many changes to their prompt. Yet, the questions developers ask to update their prompt is unknown, despite the answers to these questions affecting how developers plan their changes. With the growing number of research and commercial prompt programming tools, it is unclear whether prompt programmers' needs are being adequately addressed. We address these challenges by developing a taxonomy of 25 tasks prompt programmers do and 51 questions they ask, measuring the importance of each task and question. We interview 16 prompt programmers, observe 8 developers make prompt changes, and survey 50 developers. We then compare the taxonomy with 48 research and commercial tools. We find that prompt programming is not well-supported: all tasks are done manually, and 16 of the 51 questions -- including a majority of the most important ones -- remain unanswered. Based on this, we outline important opportunities for prompt programming tools.