BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks

TL;DR

BISCUIT introduces ephemeral UI scaffolds in computational notebooks to improve understanding, guide, and facilitate exploration of LLM-generated code, especially aiding novices in machine learning tutorials.

Contribution

This work presents a novel workflow and JupyterLab extension that adds ephemeral UIs to LLM code generation, enhancing user understanding and interaction.

Findings

BISCUIT helps users understand LLM-generated code.

It reduces prompt engineering complexity.

It enables exploration and iteration with code.

Abstract

Programmers 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 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 a user study where 10 novices used BISCUIT for machine learning tutorials, we found that BISCUIT offers users representations 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.