Disciplined AI Development
Systematic constraints and behavioral enforcement for AI collaboration
Origin & Development
The methodology emerged from extensive trial and error in AI-assisted development.
The Problemα
What led to creating this methodology:
- Constantly restating preferences and architectural requirements to AI systems
- AI consistently producing bloated monolithic code or underdeveloped implementations
- Pattern matching more effective when focused on underlying software principles rather than surface-level syntax
- Functions with multiple simultaneous questions overwhelming the system
The Breakthroughβ
Everything ultimately transpiles to binary - a series of 'can you do this? → yes/no' decisions. This insight shaped the approach:
- Instead of issuing commands, ask focused questions in proper context
- Rather than mentally managing complex setups alone, collaborate with AI to devise systematic plans
- AI systems will always tend to drift even under constraints, but they're significantly more accurate with structured boundaries
- Occasionally reminding the AI of its role prevents deviation
Discovery Processγ
These constraints were discovered through extensive trial and error:
Maintenance Hell
Tired of responses filled with excessive praise and verbose fluff that wastes time, tokens, and patience.
Effective Constraints
Instead of venting frustration, found methods that actually work for structured AI collaboration.
Methodology Specifics
Common questions about specific methodology constraints and their rationale.
Why 150 Lines?δ
Multiple benefits from this constraint:
Readability
Easy to read and understand at a glance
Modularity
Forces proper separation of concerns
Context Retention
Optimal AI context window utilization
Maintenance
Simple to maintain and debug
Testing
Easy to test individual components
Reusability
Encourages reusable, focused modules
Why Phase 0 First?ε
From meta-principles of software:
- If it displays, it must run
- If it runs, it can be measured
- If it can be measured, it can be optimized
- If it can be optimized, it can be reliable
- If it can be reliable, it can be trusted
Testing During Developmentζ
By testing during development, you catch inefficiencies early. Integration from the start means simply hooking up new components and running tests - minimal time investment with actual value returned.
- A single line modification might cause 300ms boot time regression
- Validation and consistency throughout development over programming blind
- Performance regression detection on every change
Personal Practice
How the methodology is applied in practice.
Adherenceη
Since creating the documentation, deviation hasn't occurred:
- When the model produces more lines than restricted, immediately interrupt generation
- Flag violations: '‼️ ARCHITECTURAL VIOLATION, ADHERE TO PRINCIPLES ‼️'
- Provide method instructions again depending on context storage and model
Deviation Effectsθ
What happens when things start to degrade:
- Genuine discomfort when seeing things become tangled
- Compulsive need to organize and optimize
- Deviation simply isn't an option anymore
Project Adaptationι
How to handle projects that don't fit the methodology:
- Adapt them to fit, or adjust the method itself
- This is one methodology - countless variations can be generated as needed
- 6700+ hours in AI interactions across multiple domains enables creating adjusted methodologies on demand