A Software Development Methodology for Disciplined LLM Collaboration

This may produce some successes, but it's so much more work than just writing the code yourself that it's pointless. This structured way of working with generative AI is so strict that there is no scaling it up either. It feels like years since this was established to be a waste of time.

If the goal is to start writing code not knowing much, it may be a good way to learn how and establish a similar discipline within yourself to tackle projects? I think there's been research that training wheels don't work either though. Whatever works and gets people learning to write code for real can't be bad, right?

What tends to get overlooked is the actual development speeds these projects achieve.

The PhiCode runtime for example - a complete programming language with code conversion, performance optimization, and security validation. It was built in 14 days. The commit history provides trackable evidence; manual development of comparable functionality would require months of work as a solo developer.

The "more work" claim doesn't hold up to measurement. AI generates code faster than manual typing while systematic constraints prevent the architectural debt that creates expensive refactoring cycles later. The 5-minute setup phase establishes foundations that enable consistent development throughout the project.

On scalability, the runtime demonstrates 70+ modules maintaining architectural consistency. The 150-line constraint forced modularization that made managing these components feasible - each remains comprehensible and testable in isolation. The approach scales by sharing core context (main entry points, configuration, constants, benchmarks) rather than managing entire codebases.

Teams can collaborate effectively under shared architectural constraints without coordination overhead.

This isn't about training wheels or learning syntax. The methodology treats AI as a systematic development partner focused on architectural thinking rather than ad-hoc prompting. AI handles syntax perfectly - the challenge lies in directing it toward maintainable, scalable solutions at production speed.

Previous attempts at structured AI collaboration may have failed, but this approach addresses specific failure modes through empirical measurement rather than theoretical frameworks.

The perceived 'strictness' provides flexibility within proven constraints. Developers retain complete freedom in implementation approaches, but the constraints prevent common pitfalls like monolithic files or tangled dependencies - like guardrails that keep you on the road.

The project examples and commit histories provide concrete evidence for these development speeds and architectural outcomes.