The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
When AI retrieval quality degrades despite good source material, diagnose whether notes are self-contained units or fragments requiring external context, because fragmentation produces context confusion that corrupts AI reasoning.
Match note granularity to retrieval frequency and question complexity: create fine-grained atomic notes (single claims) for domains where you need precise retrieval, and coarser aggregated notes for domains where you need high-level orientation.
When using AI systems with your knowledge base, maintain multiple granularity levels of the same material (fine-grained for precise retrieval, coarse-grained for contextual reasoning) rather than forcing a single chunk size, because different query types require different resolutions.
When using AI to analyze accumulated evidence around an open question, provide the constellation of linked notes (question + partial answers + contradictions + gaps) as context rather than asking the AI to answer from scratch, because the accumulated context enables pattern recognition your cold query cannot access.
Tag notes with 1-3 keywords answering 'If I had this insight again in a different context, what word would I search for?' rather than building taxonomies before you have enough atoms.
Favor verb-based and pattern-based tags (#deciding, #recurring-blocker) over abstract category tags (#productivity, #management) to capture actionable relationships.
When a tag appears on only one note, delete it during review; when a tag connects five notes from three different months, preserve it as earning its maintenance cost.
Index only processed permanent notes in AI-searchable systems while keeping unprocessed inbox captures outside retrieval scope, because AI systems cannot distinguish epistemic status and will retrieve raw captures with equal confidence to verified knowledge.
When recall of studied material fails, mentally reinstate the original encoding context—room, time of day, task being done, emotional state—before concluding the information wasn't learned or needs re-studying.
Feed complete externalized system context to AI assistants rather than isolated queries, because AI reasoning quality scales with the completeness and structure of the personal knowledge base it can traverse.