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Goal: Evolve the codebase into a better collaborator where artifacts enable instant AI context bootstrapping.

Written Context over Tribal Knowledge

Leaders must encourage a culture of maintaining high-level documentation that allows AI to bootstrap context instantly.

The Problem with Tribal Knowledge

Tribal knowledge lives only in people’s heads:
  • ❌ Disappears when people leave
  • ❌ Inaccessible to new team members
  • ❌ Invisible to AI assistants
  • ❌ Leads to repeated mistakes
Written context persists and scales:
  • ✅ Survives team changes
  • ✅ Onboards new members quickly
  • ✅ Enables AI to understand constraints
  • ✅ Creates institutional memory

Essential Artifacts for AI

1. Architecture.md

Purpose: High-level system overview for quick context
When to use: Every bootstrapping session should start with reading this file.

2. Specification Files (spec.md or feature.md)

Purpose: Detailed feature requirements and constraints
When to create: For any feature that will take >1 day to implement.

3. DECISIONS.md

Purpose: Document key technical decisions and their rationale
When to update: After every major technical decision.

4. GOTCHAS.md

Purpose: Document project-specific pitfalls and solutions
When to update: Immediately after debugging a non-obvious issue.

Artifacts for the Future

When a project-specific pitfall is identified during debugging, document it immediately so future AI interactions are smoother.

The Documentation Loop

  1. Encounter Problem: Hit a weird bug or edge case
  2. Debug and Solve: Figure out the root cause and fix
  3. Document Immediately: Add to GOTCHAS.md
  4. Update AI Context: Next AI session will know about this

Example: From Debugging to Documentation

During debugging:
Document immediately:
Next AI session:

Code for Collaborators

The goal is to evolve the codebase so that artifacts left behind improve the process for the whole team.

The Virtuous Cycle

  1. Engineer encounters challenge → Solves it with AI
  2. Documents solution → Updates GOTCHAS.md or ADR
  3. Next engineer → AI reads docs, avoids same pitfall
  4. Team knowledge compounds → Institutional memory grows

Cultural Shift Required

Old mindset: “I’ll just remember this” New mindset: “I’ll document this for future-me and the AI” Old mindset: “Documentation is separate from coding” New mindset: “Documentation IS part of the deliverable” Old mindset: “Only code matters” New mindset: “Context artifacts multiply AI effectiveness”

Maintaining Documentation Health

Documentation Debt is Technical Debt

Treat outdated documentation as bugs:
  • Schedule regular documentation reviews
  • Update docs when changing code
  • Mark deprecated sections clearly
  • Remove outdated information

Documentation Checklist for PRs

  • Architecture.md updated if system structure changed
  • Spec files updated if requirements changed
  • DECISIONS.md updated for new technical decisions
  • GOTCHAS.md updated if non-obvious issues found
  • README updated if setup process changed

Documentation Quality Metrics

Track documentation health:
  • Last updated date for each artifact
  • Coverage: do all major systems have docs?
  • Accuracy: audit against actual code
  • Usefulness: does AI successfully use these artifacts?

Key Principle: Written context beats tribal knowledge. Document for future-you, future-teammates, and future-AI. The artifacts you leave behind multiply everyone’s effectiveness.