Beyond Facts: A Four-Type Knowledge Framework to Fix LLM Wikis

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AI researcher and developer Andrej Karpathy's LLM Wiki concept enables users to build personal knowledge bases by having large language models extract and link concepts from raw material. A developer who built a 100-page wiki using this approach found that the system could recall facts accurately but lacked the judgment needed to act as an effective tutor or advisor. This limitation stems from the wiki storing only declarative knowledge — definitions and facts — while missing three other critical types: procedural knowledge (expert reasoning sequences), experiential knowledge (complete worked examples showing real mistakes), and interaction knowledge (knowing what to ask and when). Research on real teaching sessions supports this gap, with trained teaching assistants defaulting to direct answers over Socratic questioning under time pressure, despite knowing the method. The author argues that LLM knowledge systems must be redesigned to explicitly capture and store all four knowledge types to bridge the gap between knowing and executing under real-world conditions.

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