The author describes a problem with AI assistants loading an excessive amount of rules, regardless of the user's current task. This "always-loaded" approach, exemplified by Salesforce's extensive rulebook, leads to a diminished signal-to-noise ratio and increased costs due to prompt caching. The proposed solution involves context-aware rules, where a manifest file maps project paths to specific rule sets, ensuring only relevant rules are loaded for each session. This approach extracts approximately 29% of the original rules into scope-specific files, improving efficiency and relevance. AI
IMPACT Highlights the need for more efficient context management in AI assistants to improve user experience and reduce computational overhead.
RANK_REASON The item is a personal reflection and proposed solution to a common issue in AI assistant design, rather than a release or research paper.
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