I Was Asking the Wrong Question About AI Coding

2026-07-20 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

An editorial analyst recounts a two-day coding experience where an AI-assisted project, initially deemed successful, proved unsuitable as a foundation for future work. The author's evolving AI workflow, which transitioned from utility functions to larger feature implementations, significantly accelerated project completion. However, this speed led to a codebase optimized for immediate functionality rather than long-term reusability and architectural clarity. This experience highlighted a critical oversight: focusing on "Should AI write your code?" rather than "What are you trying to build?" The author concludes that defining the project's purpose upfront is crucial for guiding AI integration, allowing for different approaches for prototypes versus foundational systems, and ultimately ensuring AI amplifies a well-defined workflow.

Key takeaway

For AI Engineers or Software Architects planning new projects, you should prioritize defining the project's long-term purpose before integrating AI into your coding workflow. If building a foundational system, establish architectural structure early and provide AI with explicit, consistent guidance on desired patterns, even if it means repeating instructions. Conversely, for rapid prototypes, leverage AI for maximum speed. Your upfront strategic thinking prevents future refactoring debt and ensures AI effectively accelerates your intended outcome.

Key insights

Define project purpose first to effectively guide AI coding workflows and optimize for long-term reusability or rapid prototyping.

Principles

• AI amplifies defined workflows.
• Project purpose guides AI integration.
• Establish structure early for reuse.

Method

Adapt AI coding workflow by first asking "What are you trying to build?" For prototypes, prioritize speed; for foundational projects, establish structure early and provide AI clear, consistent guidance.

In practice

• Use AI for rapid prototypes.
• Provide AI clear, consistent guidance.
• Reorganize AI-generated code for reuse.

Topics

• AI Coding
• Software Development Workflow
• Code Reusability
• Project Planning
• Generative AI

Best for: Machine Learning Engineer, Software Engineer, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.