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Why CLAUDE.md keeps growing, and how rule rationale fixes it

Source
AlphaSignal
Date
AlphaSignal@AlphaSignalAI

An agent repeats a mistake, so you add a rule. Months later, nobody remembers why it exists. “Why Does CLAUDE.md Keep Growing?” calls this “catastrophic remembering.” The study tracks 247,694 instruction lifetimes across 1,867 repositories. Files like CLAUDE.md more than triple over their lifetime. The cause is lost reasoning, not changing requirements. Without the reason, deleting anything feels risky. The paper tests attaching a comment to each rule. Those comments record: > The failure behind the instruction > The hypothesis behind the fix > Evidence from previous attempts The author built tests where the ideal prompt was known. Uncommented prompts ended 211% longer than that ideal. Informative comments cut the excess to 1.4%. The agent followed constraints equally well both ways. In an interview with AlphaSignal, author Kushal Chakrabarti (@kushalc) recommends a cache approach. “The cache should have triggers and pointers, not summaries.”

Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Why it matters

instruction files like CLAUDE.md grow because nobody remembers why each rule exists. Attaching the failure, hypothesis and evidence behind each rule cut excess prompt length from 211% to 1.4% without hurting compliance.

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