Prompt Engineering Refactoring AI Code

AI-Generated Code Cleanup — Deflate the Generated Bloat

Generated code duplicates instead of extracting, wraps without deciding, and defends against impossible states. This prompt hunts those exact failure modes — without preserving bugs as behavior.

Overview

AI-generated code has a recognizable signature: repetition where a human would extract, abstraction layers that make no decision, defensive checks for states that cannot occur, and over-general solutions to specific problems. This prompt configures the contract for that material — the AI-generated context hunts each failure mode by name, and adds the rule that makes this cleanup different from other refactors: generated code can look right while being subtly wrong, so the model must verify the code does what it appears to do, and a discovered bug gets flagged rather than silently preserved as behavior. Simplification priorities do the deflating; the safety rules keep it honest.

Workflow

  1. Hunt the signature

    Repetition, over-abstraction, impossible-state checks — the known failure modes of generated code, by name.

  2. Deflate, don't redesign

    Simplification priorities remove the bloat; the non-goals keep the cleanup from becoming a rewrite.

  3. Flag what looks wrong

    Subtly-wrong generated logic gets flagged as a question — never silently preserved, never silently fixed.

Why This Works

  • Naming generated code's specific failure modes beats generic cleanup instructions
  • The looks-right-but-wrong rule addresses what makes generated code uniquely risky
  • Flag-don't-fix keeps the cleanup a refactor instead of an uncontrolled patch

Best for

  • Code accepted from AI assistants under deadline pressure
  • Vibe-coded prototypes being promoted to real modules
  • Teams adding a cleanup pass to their AI-assisted workflow

Not for

  • Judging whether the generated code is acceptable at all — that's the Code Review Prompt Generator
  • Validating the generated code's output format — that's the AI Output Validator

Use cases

  • Consolidating the near-identical handlers a generator produced
  • Removing wrapper layers and single-implementation interfaces
  • Deleting defensive checks for states that cannot occur

Tip: Save time by exploring related resources and tools that integrate with this workflow.

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