When to Delete AI-Generated Code: A Vibe Coding Cleanup Guide

There is a moment familiar to anyone who has spent a weekend "vibe coding" with an AI assistant. You started with a clean prompt, got a working function, then kept nudging the model until your project ballooned into 4,000 lines you no longer fully understand. It runs. It even passes a couple of tests. But something feels off, and you cannot tell whether the code is a solid foundation or a haunted house.

Here is the surprising part: a 2024 GitClear analysis of 153 million changed lines of code found that "code churn" (lines revised or reverted within two weeks of being written) was projected to double compared to the pre-AI baseline. In plain terms, AI-generated code is thrown away far more often than hand-written code. That is not a failure of AI. It is a signal that the delete key is now one of the most important tools in your workflow.

This guide is about vibe coding cleanup: knowing exactly when AI-generated code should be refactored, quarantined, or deleted outright. We will cover the warning signs, a repeatable decision framework, a worked before/after example, and a comparison of cleanup strategies so you can ship something you actually trust.

Key Takeaways

• Delete AI code aggressively when you cannot explain what it does line by line. Comprehension debt is worse than technical debt.
• Treat any generated code that touches auth, payments, or file I/O as guilty until proven safe. Verify before you keep.
• Use the "3-strike rule": if a snippet fails review, testing, and a rewrite attempt, delete it and re-prompt from scratch.
• Duplicated helper functions and phantom dependencies are the most common cleanup targets in vibe-coded projects.
• Cleanup is cheaper early. A 200-line delete on day two beats a 2,000-line untangling on day thirty.
• Version control is your safety net. Commit before you cut so nothing is truly lost.

What "Vibe Coding" Actually Produces

Vibe coding, a term popularized in early 2025, describes building software by describing intent in natural language and accepting whatever the model returns, often without reading every line. It is fast and genuinely fun. It is also how you accumulate code you never actually wrote.

The output has predictable characteristics. Understanding them tells you where to look during cleanup:

• Over-abstraction. Models love to invent config layers, factory functions, and wrappers for problems that needed ten lines.
• Duplicated logic. Ask for three features in three sessions and you often get three slightly different date-formatting helpers.
• Phantom dependencies. The model imports a library that half-exists, or references an API method that was deprecated two versions ago.
• Confident wrongness. Code that looks idiomatic, runs without errors, and quietly does the wrong thing on edge cases.
• Security shortcuts. Hardcoded secrets, missing input validation, and permissive CORS settings that "just work" in a demo.

None of this means AI code is bad. It means AI code needs a curator. If you want a deeper checklist for the verification side of that job, our guide on how to verify AI-generated code before you ship it pairs directly with this cleanup workflow.

The 7 Signs AI-Generated Code Should Be Deleted

Not every messy function deserves the axe. But these seven signals are strong indicators that deletion beats repair.

1. You cannot explain it out loud

If you can't narrate what a block does to an imaginary junior developer, you have comprehension debt. Code you don't understand is code you cannot maintain, secure, or debug at 2 a.m. Delete it and re-prompt with a tighter spec.

2. It duplicates something you already have

Search your codebase before keeping any new helper. If the model just rewrote your existing formatCurrency() under a new name, delete the duplicate and point to the original.

3. It touches security-sensitive surfaces

Authentication, payment handling, file uploads, and database queries generated in a single vibe session are the highest-risk artifacts in your project. When in doubt, delete and rebuild deliberately. This is doubly true for CMS work, where a single sloppy handler can expose an entire site. Our walkthrough on how to detect and contain an exploited WordPress plugin fast shows how quickly one bad snippet becomes an incident.

4. The tests are as fake as the code

AI often generates tests that assert the code does what the code does, not what it should do. If a test would pass even when the function is wrong, delete both the test and its assumptions.

5. It carries phantom or bloated dependencies

A 12KB feature that pulls in a 2MB library is a cleanup target. So is any import you cannot trace to a real, maintained package. Unvetted dependencies are a supply-chain risk, which is exactly why we recommend reading how to vet open-source software for supply chain risks before you keep any generated package.json line.

6. It "works" but you don't know why

Cargo-cult code. It compiles, the demo runs, and there is a suspicious try/catch swallowing every error. If removing a line breaks things in ways you can't predict, the code is unstable, not finished.

7. It fights your architecture

If a snippet introduces a state pattern, a naming convention, or a data flow that contradicts the rest of your app, keeping it means two codebases pretending to be one. Delete and conform.

The Vibe Coding Cleanup Decision Framework

Here is the repeatable process I run on every AI-heavy project before it ships. It takes discipline, not genius.

1. Commit first. Run git add -A && git commit -m "pre-cleanup snapshot". Nothing you delete is ever truly gone, so you can cut fearlessly.
2. Map the surface. Generate a quick file tree and mark every module that touches auth, money, user input, or the network. These get the strictest review.
3. Read every function once. Not skim. Read. Flag anything you can't explain with a // REVIEW comment.
4. Apply the 3-strike rule. For each flagged block: (1) can you fix it in under five minutes? (2) does a real test pass afterward? (3) if not, can you rewrite it cleanly from a tighter prompt? Three strikes and you delete it.
5. Deduplicate. Grep for repeated logic (grep -rn "function format") and collapse duplicates into shared utilities.
6. Prune dependencies. Run your package manager's "why is this here" command (npm ls <pkg>) and delete anything unused.
7. Re-test and diff. Compare against your snapshot. If behavior is intact and the line count dropped, you did it right.

The 3-strike rule is the heart of it. It stops you from sinking an hour into repairing forty lines that a fresh, well-scoped prompt could regenerate correctly in ninety seconds.

A Worked Example: Cleaning a 1,200-Line Vibe Project

Let me make this concrete. Say you vibe-coded a small invoicing web app over a weekend. It ended up at 1,247 lines across 14 files. Here is what a real cleanup pass looked like on a project like this.

Before cleanup:

• 1,247 lines of code, 14 files
• 3 different date-formatting functions (fmtDate, toNiceDate, dateHelper)
• 2 unused npm packages (a 340KB charting lib imported once and never rendered)
• 1 auth middleware with a hardcoded fallback token
• 18 tests, of which 6 asserted nothing meaningful

The cleanup:

1. Collapsed 3 date functions into 1 shared utility. Removed 41 lines.
2. Deleted the charting library and its dead import. Shed 340KB from the bundle.
3. Deleted the hardcoded token, replaced with an environment variable check that throws on startup if missing. Removed a critical vulnerability.
4. Deleted 6 hollow tests, rewrote 3 to test real edge cases (empty invoice, negative amount, missing customer).
5. Removed two over-engineered "factory" wrappers around simple object creation. Removed 88 lines.

After cleanup: 1,061 lines, one date utility, zero dead dependencies, no hardcoded secrets, and a test suite that would actually catch a regression. That is 186 fewer lines and one fewer way to get breached, in about 40 minutes of focused work.

The lesson: the cleanup didn't rewrite the app. It removed the parts that were pretending to be the app.

Delete vs Refactor vs Quarantine: Choosing Your Approach

Deletion isn't the only option. Sometimes you refactor in place, and sometimes you wall off code you're not ready to touch. Here is how the three strategies compare on the criteria that actually matter.

Criteria	Delete & Re-prompt	Refactor In Place	Quarantine
Best for	Code you don't understand or trust	Mostly-good code with local mess	Working code you can't verify yet
Time cost	Low to medium	Medium to high	Very low upfront
Risk of hidden bugs	Low (fresh, scoped output)	Medium (you keep assumptions)	High (deferred, not solved)
Comprehension gained	High	High	Low
Reversibility	High (with git)	Medium	High
Security payoff Cover image: The Torch Graduate circuit board (bottom) by Chris Whytehead, licensed under BY-SA 3.0 via Openverse.