A Software Development Methodology for Disciplined LLM Collaboration

Summary

Disciplined AI Software Development - Collaborative A structured approach for working with AI on development projects. This methodology addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints. The Context Problem AI systems work on Question → Answer patterns. When you ask for broad, multi-faceted implementations, you typically get: Functions that work but lack structure Repeated code across components Architectural inconsistency over sessions Context dilution causing output drift More debugging time than planning time How This Works The methodology uses four stages with systematic constraints and validation checkpoints. Each stage builds on empirical data rather than assumptions. Planning saves debugging time. Planning thoroughly upfront typically prevents days of fixing architectural issues later. The Four Stages Stage 1: AI Configuration Set up your AI model's custom instructions using AI-PREFERENCES.md. This establishes behavioral constraints and uncertainty flagging with ⚠️ indicators when the AI lacks certainty. Stage 2: Collaborative Planning Share METHODOLOGY.md with the AI to structure your project plan. Work together to: Define scope and completion criteria Identify components and dependencies Structure phases based on logical progression Generate systematic tasks with measurable checkpoints Output: A development plan following dependency chains with modular boundaries. Stage 3: Systematic Implementation Work phase by phase, section by section. Each request follows: "Can you implement [specific component]?" with focused objectives. File size stays ≤150 lines. This constraint provides: Smaller context windows for processing Focused implementation over multi-function attempts Easier sharing and debugging Implementation flow: Request specific component → AI processes → Validate → Benchmark → Continue Stage 4: Data-Driven Iteration The benchmarking suite (built first) provides performance data throughout...