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The Problem with "Just Generate It"

Most teams start AI development with a workflow like:

This appears efficient.

Unfortunately, it often produces:

  • Architectural inconsistencies
  • Missing requirements
  • Duplicate functionality
  • Poor separation of concerns
  • Excessive refactoring

The root problem is simple:

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Generation starts before understanding.

Successful engineering teams spend far more time defining the problem than writing code. The same principle applies to AI-assisted development.

Mini Exercise​

Take a "just generate it" request and break it into separate phases before any code is written.

Example​

Instead of Build a customer dashboard, first define the domain, use cases, and success criteria, then generate the architecture, validate the result, and refine it afterward.