Learning Document

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A Learning Document (also: lesson-learned document, L-doc) is a Document that captures a single, durable learning extracted from operational experience for later reference and reuse.

  • AKA: L-doc, Lesson-Learned Document.
  • Context:
    • It can function as a unit of organizational memory persisting beyond ephemeral session context.
    • It can bridge tacit knowledge (lived experience) and explicit knowledge (knowledge-base-storable form).
    • It can be designed for accretion: many small documents indexed and cross-linked rather than monolithic reports.
    • It can be distinguished from a Post-Mortem (incident-scoped, multi-finding) and a Research_Paper (external-audience, evidence-comprehensive).
    • It can be characterized by: atomicity (one document captures one learning), stable identification (citable from other artifacts), provenance (originating context recorded), and an explicit status lifecycle (Draft → Active → Superseded or Archived).
  • Example(s):
    • The AGET framework's evolution files (on the order of 1,000 documents as of 2026) — each capturing one operational learning from human-AI collaborative coding sessions.
    • Engineering postmortem follow-up files when split into atomic learnings per finding.
  • Counter-Example(s):
    • A Post-Mortem covering multiple findings from one incident — multi-learning by design.
    • A Status_Report — present-state-oriented, not learning-extraction-oriented.
    • An Architecture_Decision_Record — captures a decision and rationale, not a learning extracted from outcomes.
  • See: Document, Knowledge_Base, Organizational_Learning, Lessons_Learned, Software Session.

References