Novices frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, we introduce a novel workflow into computational notebooks that augments LLM-based code generation with an additional ephemeral UI step, offering users UI-based scaffolds as an intermediate stage between user prompts and code generation. We present this workflow in BISCUIT, an extension for JupyterLab that provides users with ephemeral UIs generated by LLMs based on the context of their code and intentions, scaffolding users to understand, guide, and explore with LLM-generated code. Through 10 user studies where novices used BISCUIT for machine learning tutorials, we discover that BISCUIT offers user semantic representation of code to aid their understanding, reduces the complexity of prompt engineering, and creates a playground for users to explore different variables and iterate on their ideas. We discuss the implications of our findings for UI-centric interactive paradigm in code generation LLMs.

通过在计算笔记本中引入一种新的工作流程，将基于大型语言模型的代码生成与额外的临时 UI 步骤相结合，我们提供了一种新方法来帮助初学者理解和使用由大型语言模型生成的代码，并为用户提供基于用户交互的 UI 来辅助理解、指导和探索由大型语言模型生成的代码。

BISCUIT: 在计算笔记本中使用临时UI支撑LLM生成的代码