Vibe Coding is Fun — But Who's Checking the AI's Work?

What Is Vibe Coding?

Vibe coding is the practice of building software by describing what you want in plain language and letting an AI assistant — GitHub Copilot, Claude, Cursor, or ChatGPT — write the actual code. You prompt, it generates, you paste, it (sometimes) works. The term was coined by Andrej Karpathy in early 2025 and it stuck because it perfectly captures the feeling: you are surfing the AI's output rather than writing line by line.

For prototypes, side projects, and glue code, vibe coding is genuinely great. The problem starts when the vibe hits production.

Why AI Code Fails in Ways You Don't Expect

AI code generators are trained to produce plausible-looking code, not necessarily correct code. The distinction matters more than most people realize:

• Confident hallucinations: The model invents function names, API endpoints, or library methods that don't exist — with no warning, no error, no caveat.
• Stale knowledge: The model's training data has a cutoff. It may generate code for a library version that deprecated the function it's calling two releases ago.
• Context blindness: AI doesn't know your production database schema, your rate limits, or that the endpoint it just wrote bypasses your auth middleware.
• Copy-paste accumulation: Each generation looks fine in isolation. Three rounds of "make it better" later and you have contradictory logic that no single prompt introduced.

The 5-Minute Vibe Code Audit

You don't need a full code review process. You need five habits, each taking under a minute:

1. Diff Every AI Edit Before Committing

Before you git add anything, paste the original and the AI version into a Code Diff Checker. Read every red line. AI edits look surgical but often touch more than you asked for — a changed default value here, a removed validation there. The diff makes the invisible visible.

→ Related: How to Compare Code Files Online: A Complete Guide

2. Validate Every JSON Structure

If your AI generated an API response handler, a config file, or any JSON — paste it into a JSON Formatter before trusting it. AI-generated JSON frequently has trailing commas, mismatched brackets, or wrong data types that look correct in a dark-themed code editor at midnight.

→ Related: The Complete Guide to JSON Formatting

3. Test Every Regex Pattern

AI-generated regex looks authoritative. It is often wrong for edge cases. Paste every regex your AI writes into a Regex Tester and test it against at least 5 inputs: the happy path, an empty string, a very long string, a string with special characters, and a string that should NOT match. You will catch problems 30% of the time.

→ Related: The Complete Regex Guide

4. Grep for Hardcoded Secrets

This is the one that gets people fired. AI assistants helpfully fill in placeholder values using your actual keys from context — API keys, database passwords, JWT secrets. Before committing, search for strings like sk-, Bearer , password =, secret =. Use a Password Strength Checker on any string that looks suspiciously long and random — it will tell you if it matches the entropy profile of an API key.

→ Related: Password Security: The Complete Guide

5. Read the Logic Once

Not the syntax — the logic. Ask yourself: what happens when the input is empty? What happens when the API call fails? What happens when this runs twice? AI-generated code optimizes for the happy path. It rarely handles edge cases unless you explicitly asked for them. One slow read through the generated function, thinking about what could go wrong, catches more bugs than any tool.

The Bigger Picture

Vibe coding is not going away. It is genuinely productive for the right tasks. The developers who get burned are not the ones who use AI — they are the ones who use AI and forget to look. The five-minute audit above is not about slowing down. It is about staying in control of code that has your name on it when it breaks at 3am.

The tools that help you verify AI output — diff checkers, JSON validators, regex testers — were built for debugging human code. They work just as well on machine code. Use them.

Frequently Asked Questions

Is vibe coding safe to use in production?

It can be, with proper review. The risk isn't the AI itself — it's shipping code you haven't read. Treat AI output the same way you'd treat code from a junior developer: review it before it merges.

Which AI tools are best for vibe coding?

GitHub Copilot, Cursor, Claude, and ChatGPT are the most popular in 2026. The tool matters less than your review habits — all of them make the same categories of mistakes.

How do I catch hallucinated function names?

Run the code, check your linter, and read the imports. If the AI imported a function from a package, verify that function actually exists in the current version of that package. npm/pip/cargo docs are your friend.

Should I tell my team I used AI to write code?

Yes. This is becoming a standard part of pull request descriptions in most teams. "Generated with Copilot, reviewed manually" is the same as "scaffolded with a generator, reviewed manually." Transparency beats surprise.

Can AI review its own code?

Sometimes. Asking the AI to "review the code you just wrote for security issues" catches obvious problems. But it has the same blind spots as the generation step — it won't know your production context, your auth model, or that a function it considers safe bypasses your middleware.