In my work I use AI for code generation in two distinct ways:
- For throwaway prototypes I pretty much vibe code, only making manual tweaks occasionally if the agent gets stuck or something looks wrong visually.
- For production code I stay in the loop, reviewing and iterating on the code with an agent.
This leads to me having experience with “messy” prototype code and “maintainable” production code. Whenever my prototype code lives longer than expected it gets to a point where the
agent starts introducing regressions or churning on changes.
This happens much less often with the “maintainable” code. From my perspective it seems pretty clear that LLMs are faster, more effective, and less error-prone in a well structured codebase
- For throwaway prototypes I pretty much vibe code, only making manual tweaks occasionally if the agent gets stuck or something looks wrong visually. - For production code I stay in the loop, reviewing and iterating on the code with an agent.
This leads to me having experience with “messy” prototype code and “maintainable” production code. Whenever my prototype code lives longer than expected it gets to a point where the agent starts introducing regressions or churning on changes.
This happens much less often with the “maintainable” code. From my perspective it seems pretty clear that LLMs are faster, more effective, and less error-prone in a well structured codebase