Intent-driven AI development often favors procedural coding over upfront abstraction. You write a straightforward, linear solution that works now, then let AI refactor it into reusable modules when real patterns appear.
Why Procedural Works for AI
Procedural code is explicit, sequential, and low on hidden state. AI can reason about it more easily because behavior is visible and localized. When abstraction is needed, AI can derive it from repetition rather than speculation.
The Flow Advantage
You read procedural code top-to-bottom like a story. There is less context switching and less dependency hunting. This keeps you in flow, especially when you are iterating quickly.
Refactoring as a Background Process
AI can analyze procedural code, identify repeated logic, and extract functions or modules without human effort. You don’t have to predict the future. You allow the system to evolve based on actual usage.
Example Pattern
- Write a direct, working script.
- Observe repeated blocks.
- Ask AI to extract them into functions.
- Validate with tests.
Risks and Mitigations
- Risk: Procedural code grows unwieldy. Mitigation: scheduled AI refactoring passes.
- Risk: Hidden dependencies emerge. Mitigation: add quick verification tests.
- Risk: Refactoring introduces bugs. Mitigation: automate tests before and after.
Procedural-first development embraces speed and clarity while preserving the ability to formalize structure later. It is a pragmatic approach that aligns with AI’s strengths.