Disciplined AI Software Development: A Structured Methodology for AI Collaboration in Software Projects
By
jay-baleine
Crisp on the outside, thoughtful on the inside. A keeper.
Summary
This article presents a structured methodology called 'Disciplined AI Software Development' for collaborating with AI systems on software development projects. The approach addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints. It uses a four-stage process that builds on empirical data rather than assumptions, with the core insight that planning saves debugging time. The methodology specifically tackles the 'context problem' where AI systems operate on Question → Answer patterns and struggle with broad, multi-faceted implementations.
Key quotes
· 4 pulledThis methodology addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints
AI systems work on Question → Answer patterns. When you ask for broad, multi-faceted implementations, you typically get:
The methodology uses four stages with systematic constraints and validation checkpoints. Each stage builds on empirical data rather than assumptions
Planning saves debugging time
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