The author explores the challenge of writing README files that effectively serve both human readers and Large Language Models (LLMs). They found that LLMs tend to produce verbose text, including unnecessary explanations, because they don't grasp implicit human context. To address this, the author experimented with separating content for humans and LLMs, placing LLM-specific information in a folded section. However, this approach proved difficult, as the author's own reading habits and revision process didn't easily translate into sentence-level rules for LLM content. Ultimately, the author realized they had been optimizing READMEs for LLM crawlers, assuming repositories were cloned more often than viewed by humans, a premise that has recently changed. AI
IMPACT Developers may need to adapt documentation strategies to effectively communicate with both human users and AI tools.
RANK_REASON The item is an opinion piece discussing the challenges of writing documentation for both humans and LLMs.
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