The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Feed your operational definitions to AI systems as explicit context before generating analysis or recommendations, treating your personal glossary as the translation layer between the model's probability-weighted semantics and your specific conceptual framework.
Before prompting AI to analyze meeting transcripts or documents, explicitly request separated outputs: first section lists only observable facts without interpretation, second section offers interpretations of those observations.
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.
Position your system prompt's most important instructions in the first 20% of tokens, because transformer attention mechanisms allocate disproportionate processing to early sequence positions through positional encoding and causal masking.