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arXiv · 2504.09798

ReadMe.LLM: A Framework to Help LLMs Understand Your Library

Sandya Wijaya·Jacob Bolano·Alejandro Gomez Soteres·Shriyanshu Kode·Yue Huang·Anant Sahai
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Abstract

Large Language Models (LLMs) often struggle with code generation tasks involving niche software libraries. Existing code generation techniques with only human-oriented documentation can fail -- even when the LLM has access to web search and the library is documented online. To address this challenge, we propose ReadMe$.$LLM, LLM-oriented documentation for software libraries. By attaching the contents of ReadMe$.$LLM to a query, performance consistently improves to near-perfect accuracy, with one case study demonstrating up to 100% success across all tested models. We propose a software development lifecycle where LLM-specific documentation is maintained alongside traditional software updates. In this study, we present two practical applications of the ReadMe$.$LLM idea with diverse software libraries, highlighting that our proposed approach could generalize across programming domains.

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Sandya Wijaya, Jacob Bolano, Alejandro Gomez Soteres, Shriyanshu Kode, Yue Huang, Anant Sahai. 2025-04-14. ReadMe.LLM: A Framework to Help LLMs Understand Your Library. https://arxiv.org/abs/2504.09798

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