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Debugging
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Refactor Messy AI Code

Clean up AI-generated spaghetti safely: dedupe, extract, and organize without breaking working features.

6 steps18 verify checks60-120 minutesWorks with: emergent · chatgpt · claude · cursor
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The Route

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Context Pack

Paste this first. It briefs the AI on requirements, constraints, and the Definition of Done before your first build prompt.

Context Pack
PROJECT CONTEXT:
My AI-built codebase works (mostly) but is a mess: duplicated logic, thousand-line files, dead code, inconsistent patterns.

GOAL:
Refactor to organized, deduplicated, consistently-patterned code while keeping every feature working.

REQUIREMENTS:
- A behavior baseline captured before touching anything
- Duplication and dead code eliminated
- Oversized files split into modules/components
- Consistent patterns for API calls and state
- Baseline re-verified after each refactor stage

CONSTRAINTS:
- Never refactor and change behavior in the same step
- One kind of change per pass - verify between passes
- If it was not broken, it must not be broken after

DEFINITION OF DONE:
- All baseline behaviors still pass
- No duplicated business logic remains
- No file exceeds a sane size with mixed concerns
- API/state patterns are consistent codebase-wide

COMMON FAILURES TO AVOID:
- Refactors that quietly delete edge-case handling
- Renames that break imports at runtime
- Consolidations that merge two subtly different behaviors into one wrong one
- Giant single-commit rewrites nobody can verify

Paste this into your AI builder first. It teaches the AI what you want before you give it the build prompt.

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Made with Emergent