Disciplined AI Development
Systematic constraints and behavioral enforcement for AI collaboration
Stage 1: AI Behavioral Configuration
Deploy systematic behavioral consistency and constraint enforcement.
Configuration Stepsα
Set up AI behavioral constraints before beginning work:
- Configure AI Custom Instructions with
AI-PREFERENCESto establish behavioral constraints and uncertainty flagging - Load
CORE-PERSONA-FRAMEWORKwith domain-appropriate persona (GUIDE,TECDOC,R&D,MURMATE, or custom) - Activate Persona with command:
Simulate Persona
Stage 2: Collaborative Planning
Share METHODOLOGY document with AI to structure your project plan collaboratively.
Planning Processβ
Work with AI to establish project structure:
- Define scope and completion criteria
- Identify components and dependencies
- Structure phases based on logical progression
- Generate systematic tasks with measurable checkpoints
Planning ProcessOUTPUT: A development plan following dependency chains with modular boundaries.
PAG Alternativeγ
For explicit structure, express your plan using PAG phases with validation gates:
PAG Alternative# PHASE 1: Core Implementation ## PURPOSE Implement primary application logic. ## DEPENDENCIES Phase 0 infrastructure complete. ## VALIDATION GATE ✅ All core functions implemented ✅ Unit tests passing ✅ Benchmarks integrated
Stage 3: Systematic Implementation
Work phase by phase, section by section. Each request follows focused objectives.
Implementation Flowδ
Execute work in focused increments:
- Request specific component:
Can you implement [component]? - AI processes with focused context
- Validate output against constraints
- Benchmark and continue to next component
Implementation FlowCONSTRAINT: File size stays ≤150 lines for smaller context windows, focused implementation, and easier debugging.
Stage 4: Data-Driven Iteration
The benchmarking suite (built first) provides performance data throughout development.
Iteration Processε
Use data to drive optimization decisions:
- Build Phase 0 benchmarking infrastructure first
- Feed performance data back to AI for optimization decisions
- Validate architectural compliance continuously
- Iterate based on measurements rather than guesswork