After a year of using Claude Code for end-to-end operations, the effectiveness of instruction files has evolved from aspirational rules to strict, enforceable commands. The key to this transformation lies in three properties: rules must be backed by specific incidents ('scars'), they need to be mechanically precise to avoid interpretation, and the most critical rules require enforcement by automated machinery rather than relying on the model's judgment. This structured approach, involving commit hooks, fail-closed code, and detailed incident logs, ensures that crucial operational boundaries are maintained even under long context constraints. AI
IMPACT Refines best practices for instructing LLMs in production environments, emphasizing the need for specific, enforceable rules over general guidance.
RANK_REASON Article discusses practical application and refinement of instructions for an AI model, rather than a new release or core research.
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