Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.
From Computational to Conversational Notebooks
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abstract
Today, we see a drastic increase in LLM-based user interfaces to support users in various tasks. Also, in programming, we witness a productivity boost with features like LLM-supported code completion and conversational agents to generate code. In this work, we look at the future of computational notebooks by enriching them with LLM support. We propose a spectrum of support, from simple inline code completion to executable code that was the output of a conversation. We showcase five concrete examples for potential user interface designs and discuss their benefits and drawbacks. With this, we hope to inspire the future development of LLM-supported computational notebooks.
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cs.HC 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Plainbook: Data Science, in Plain Language
Plainbook makes data-science notebooks natural-language-first by preserving cell descriptions, generating code via AI, enforcing linear execution via a checkpointing kernel, and adding value-centered cell and global tests.