Disciplined AI Development
Systematic constraints and behavioral enforcement for AI collaboration
Setup Steps
Initial configuration to prepare the AI environment for disciplined development.
Setup Workflowα
Follow these steps to configure your environment:
- Step 1: Configure AI with AI-PREFERENCES as custom instructions
- Step 2: Share CORE-PERSONA-FRAMEWORK.json + selected PERSONA
- Step 3: Issue command: 'Simulate Persona'
- Step 4: Share METHODOLOGY document for planning session
- Step 5: Collaborate on project structure and phases
- Step 6: Generate systematic development plan
Execution Steps
Execute the development plan with systematic validation.
Execution Workflowβ
Follow these steps during active development:
- Step 1: Build Phase 0 benchmarking infrastructure first
- Step 2: Work through phases sequentially
- Step 3: Implement one component per interaction
- Step 4: Run benchmarks and share results with AI
- Step 5: Validate architectural compliance continuously
Quality Assurance
Continuous validation throughout the development process.
Quality Checksγ
These checks help maintain code quality:
Documentation Building Process
Systematic approach to creating project documentation that drives development.
Step 1: Project Decompositionδ
Ask yourself these questions:
- What does 'finished' look like?
- What are the major pieces that need to exist?
- What depends on what?
- Where are the natural stopping points?
Step 2: Phase Creationε
Group work into phases based on:
- Dependency chains: Things that must happen in sequence
- Logical groupings: Related functionality that makes sense together
- Natural checkpoints: Places where you can validate progress
- Risk management: Tackle uncertain parts early
Step 3: Task Breakdownζ
For each deliverable, define:
Specific Action
What exactly needs to be done
Output
What will exist when complete
Success Criteria
How to verify completion
Integration Points
How it connects to other work
Step 4: Progress Trackingη
Status indicators for tracking:
- ✅ COMPLETED: Done and validated
- 🔒 BLOCKED: Cannot proceed due to dependency
- 📋 READY: Dependencies met, can start
- ⚠️ UNCERTAIN: Need clarification or decision
Systematic Enforcement Framework
Mandatory checkpoints that prevent moving forward with incomplete work.
Architectural Complianceθ
Per-phase mandatory checkpoints:
- SoC VALIDATION: Each module single responsibility, clear boundaries
- DETERMINISTIC BEHAVIOR: Synchronous operations, predictable outcomes
- FILE SIZE COMPLIANCE: All files ≤150 lines or properly modularized
- DRY ENFORCEMENT: No duplicate code, existing functions reused
- KISS VALIDATION: Minimal complexity, surgical implementations
- CONFIG CENTRALIZATION: No hardcoded values outside constants
- PERFORMANCE INTEGRATION: Benchmarks operational, gates passing
Code Quality Gatesι
Requirements before phase advancement:
- Self-explanatory naming, no comments needed
- Performance characteristics match workload requirements
- Every addition serves core project purpose
- Regression detection prevents performance degradation
Mid-Phase Validationκ
Checks during active development:
- INCREMENTAL COMPLIANCE: Check after each significant change
- BENCHMARK INTEGRATION: New components measured immediately
- DEPENDENCY ALIGNMENT: Imports match architectural boundaries
- FEATURE CREEP CHECK: Question necessity of each addition
Success Metrics
Measurable indicators of methodology effectiveness.
Technical Indicatorsλ
Code and architecture quality metrics:
- All architectural principles consistently applied across codebase
- Performance baselines maintained throughout development lifecycle
- Zero production incidents related to architectural violations
- File size constraints adhered to without compromising functionality
Operational Indicatorsμ
System behavior under real conditions:
- System uptime and reliability under production load
- Predictable resource utilization patterns
- Graceful degradation under stress conditions
- Maintainability preserved as codebase grows
Development Indicatorsν
Process quality metrics:
- Enforcement checkpoints prevent architectural drift
- Performance regression detection catches optimizations and degradations
- Code review efficiency improved through systematic validation
- Technical debt accumulation prevented through continuous compliance