Crafting effective prompts for code generation or editing with Large Language Models (LLMs) is not an easy task. Particularly, the absence of immediate, stable feedback during prompt crafting hinders effective interaction, as users are left to mentally imagine possible outcomes until the code is generated. In response, we introduce Language-Oriented Code Sketching, an interactive approach that provides instant, incremental feedback in the form of code sketches (i.e., incomplete code outlines) during prompt crafting. This approach converts a prompt into a code sketch by leveraging the inherent linguistic structures within the prompt and applying classic natural language processing techniques. The sketch then serves as an intermediate placeholder that not only previews the intended code structure but also guides the LLM towards the desired code, thereby enhancing human-LLM interaction. We conclude by discussing the approach's applicability and future plans.

翻译：在为大型语言模型（LLMs）编写代码生成或编辑的有效提示时并非易事。特别是在提示编写过程中缺乏即时稳定的反馈会阻碍有效交互，因为用户只能依靠心理想象来预判可能的结果，直至代码生成。为此，我们提出面向语言的代码草图方法，这是一种在提示编写过程中以代码草图（即不完整的代码大纲）形式提供即时增量反馈的交互式方法。该方法通过利用提示中固有的语言结构并应用经典自然语言处理技术，将提示转换为代码草图。该草图随后作为中间占位符，既能预览预期代码结构，又能引导大语言模型生成所需代码，从而增强人机交互。最后我们讨论了该方法的适用性和未来计划。