A Software Development Methodology for Disciplined LLM Collaboration
4 days ago
- #Software Methodology
- #Systematic Constraints
- #AI Development
- Disciplined AI Software Development Methodology addresses code bloat, architectural drift, and context dilution through systematic constraints.
- AI systems operate on Question → Answer patterns, leading to unstructured functions, repeated code, and architectural inconsistency without proper constraints.
- The methodology involves four stages: AI configuration, collaborative planning, systematic implementation, and data-driven iteration.
- Planning upfront saves debugging time by preventing architectural issues later.
- AI-PREFERENCES.md and METHODOLOGY.md are used to set behavioral constraints and structure project plans.
- Implementation is done phase by phase with file size constraints (≤150 lines) to maintain focus and modularity.
- Benchmarking suite is built first to provide performance data for optimization decisions.
- Project extraction tool helps in tracking architectural compliance and creating focused development context.
- The methodology reduces architectural drift and context degradation compared to unstructured approaches.
- Key principles include empirical validation, systematic constraints, and focused implementation.