Why AI Often Gets Large Refactors Wrong
AI coding tools are good at local changes. They struggle when refactors depend on hidden coupling, undocumented rules, and cross-module behavior.
0xNN · · 9 min read
AI tools are often good at local edits and weak at broad refactors. The problem is usually not syntax. The problem is implicit context: hidden dependencies, undocumented business rules, historical naming, and side effects spread across modules.
Large refactors are hard because they are about preserving behavior while changing structure. That requires more than file-level reading. It requires judgment about what must not accidentally change.
Safer ways to use AI for refactors
• break the task into smaller units
• ask the model to state its assumptions
• rely on tests as guardrails
• let AI handle mechanical updates, not architectural judgment
Recommended reading
• SWE-bench
• Anthropic Docs
• OpenAI Docs
• Martin Fowler - Refactoring
AI is useful for refactoring. It is just more useful when the task is scoped tightly and reviewed like a teammate, not trusted like an infallible maintainer.
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*Written after too many clean-looking AI diffs turned into test runs, git blame sessions, and reminders that understanding a codebase is more than reading files.*