7 AI Coding Mistakes That Will Wreck Your Project (And How to Avoid Them)

Common mistakes vibe coders make when building with AI tools. Avoid these pitfalls to ship faster and break less.

• By Keaton • 11 min read
mistakes best practices ai coding debugging vibe coding

You asked Claude to build a feature. It generated 300 lines of code. It looks good. You shipped it.

Three hours later it’s failing in production.

This is the vibe coder’s original sin: trusting the first output. AI is confident. AI is fast. AI is frequently wrong. But it’s so smooth that you don’t notice until users do.

After talking to dozens of vibe coders who’ve shipped broken code, reviewed hundreds of AI-generated projects, and personally making every mistake on this list, here are the seven things that will wreck your project. And how to not do them.

Mistake 1: Accepting the First Output Without Testing

What it looks like: Claude generates code. You look it over. It seems fine. You ship it.

Why it happens: AI is fast. The code is syntactically correct. It compiles. Your brain says “good enough” and you move on.

The problem: Syntactically correct and functionally correct are not the same thing. AI will generate code that runs without errors but produces wrong results. It’ll handle the happy path perfectly and crash on edge cases. It’ll work in development and fail in production.

How to fix it:

  1. Always run the code first. Not read it. Run it. With real inputs.

  2. Test the edges. What happens with empty inputs? Null values? Unexpected data types? Boundary conditions?

  3. Verify the output actually matches what you asked for. Not “does this look right?” but “does this do exactly what I specified?”

  4. Use console logs liberally. Add logging everywhere. Watch the data flow. See what’s actually happening versus what you expected.

  5. Walk through the logic manually. Pick a specific input. Trace it through the code step by step. Does it produce the right output?

Spend 5–10 minutes per file actually testing. It’ll save you hours of debugging later.

Mistake 2: No Version Control (Trusting AI to Remember Your Codebase)

What it looks like: You’re iterating with Claude. Each prompt generates new code. You copy-paste it into your editor. No commits. No branches. Just the latest version from Claude.

Then something breaks. You don’t know what changed. You try to revert. You can’t, because you never committed.

Why it happens: It’s fast. You’re in a flow state. Version control feels like ceremony when you’re moving quickly.

The problem: You have no history. You can’t revert. You can’t compare versions. You can’t see what changed between “working” and “broken.” And if Claude generates something you don’t like, you have no way to get back to what you had before.

How to fix it:

  1. Commit after every major change. Even if it’s broken. Even if it’s incomplete. Commit it.

  2. Write meaningful commit messages. Not “update code” but “add email validation to signup form” or “fix N+1 query in user list.”

  3. Use branches for experimental features. Main branch = always deployable. Feature branches = experimental.

  4. Review your diffs. When you commit, look at what changed. Did Claude modify code you didn’t ask it to modify? Did it introduce something unexpected?

  5. Use git in your editor. Cursor, VS Code, Windsurf — they all have git built-in. Use it. It’s frictionless.

If something breaks, you can revert instantly. If Claude goes off the rails, you have a reference point to recover from.

Mistake 3: Prompt Soup (Vague, Messy Prompts)

What it looks like: You describe what you want in informal language. No structure. No specifics.

“Add a feature that validates the data and makes sure it’s right and also logs stuff when it goes wrong.”

Claude generates something. It works. Mostly. But it doesn’t match exactly what you needed because you weren’t clear on what you needed.

Why it happens: You’re thinking out loud. You’re describing the feature as you’re thinking about it, not after you’ve thought about it completely.

The problem: Vague prompts produce vague code. Claude will fill in gaps, make assumptions, and guess at your intent. Usually it guesses wrong.

How to fix it:

  1. Write your prompt like a specification, not a conversation. Not “can you add a thing that…” but “add a function that does X, takes inputs Y, and returns Z.”

  2. Be specific about inputs and outputs. Not “validate the data” but “validate email format using RFC 5322 rules” or “validate that age is between 18 and 120.”

  3. Give examples. “If input is ‘john@example.com’, output should be true. If input is ‘john@’, output should be false.”

  4. State edge cases explicitly. “Handle null values by returning null. Handle empty arrays by returning an empty result object.”

  5. Define the scope. What should this function do? What should it NOT do? Where are the boundaries?

A 30-second prompt that’s clear beats a 5-minute prompt that’s vague. Every time.

Mistake 4: Ignoring Security (Hardcoded Keys, No Auth Validation)

What it looks like: Claude generates code. You don’t review it carefully. You deploy it. Weeks later, someone finds your API key in the code comments or discovers that your form accepts requests from anyone without validation.

Why it happens: Security feels abstract. You’re focused on shipping the feature, not on threat models. Claude generates code that works, so it must be secure, right?

The problem: No. AI will confidently generate code with security vulnerabilities: hardcoded secrets, SQL injection opportunities, missing CSRF tokens, no rate limiting, trust in user input, exposed error messages.

How to fix it:

  1. Check for hardcoded secrets. Search for API keys, passwords, tokens in the generated code. Never ship them.

  2. Verify authentication. If the code touches auth, check: Does it validate the user? Does it verify permissions? Can someone bypass it?

  3. Check input handling. How does the code handle user input? Does it trust it directly? Does it sanitize it? For database queries, is it parameterized or concatenated?

  4. Review error messages. Do errors leak information? Can you enumerate users? Can you discover system internals from error responses?

  5. Use security linters. Snyk, OWASP, Semgrep — these tools catch common vulnerabilities automatically.

  6. Assume the code is wrong. Every security review should start from: “This code probably has a vulnerability. Where is it?”

Read the Vibe Coding Security Guide before shipping anything to production.

Mistake 5: Over-Engineering (Letting AI Add Features You Didn’t Ask For)

What it looks like: You ask Claude to “add a button that saves the user’s preference.” It generates not just a button, but an entire preference system with versioning, change history, sync queues, and conflict resolution.

Most of it is well-written code. All of it is wrong.

Why it happens: AI sees patterns. It extrapolates. It thinks “user preference” means “build a robust preference system.” So it does.

The problem: You now have 10x more code than you asked for. You have more surface area for bugs. You have complexity you don’t need. You have dependencies you didn’t ask for. Maintenance burden increases. Bugs multiply.

How to fix it:

  1. Specify scope explicitly. Not “add user preferences” but “add a single boolean preference for dark mode that persists to localStorage.”

  2. Disable features you didn’t ask for. If Claude adds something extra, tell it to remove it: “Remove the sync queue. Remove versioning. Keep only the essential preference storage.”

  3. Review the code for mystery dependencies. Did it add packages? Do you need them? If not, remove them.

  4. Check for architectural changes. Did Claude refactor your codebase to fit its pattern? Did it move code around? This is a red flag.

  5. Ask the inverse question. What does this code do that I didn’t ask for? If the answer is anything, cut it out.

Your spec is what matters. Extra features are extra bugs in disguise. Ship what you asked for, not what Claude thought you needed.

Mistake 6: No Error Handling (Happy Path Only)

What it looks like: Claude generates a function that works perfectly when everything goes right. User provides valid input. Network request succeeds. Database is available. Everything works.

Then the network fails. Or the input is invalid. Or the database is down. The code crashes.

Why it happens: AI defaults to the happy path. Edge cases are harder to reason about, so AI often skips them unless you explicitly ask.

The problem: Real systems fail. Networks fail. Users provide bad input. Databases go down. If your code only handles the happy path, it crashes in production.

How to fix it:

  1. Require error handling in your prompts. “If the API request fails, log the error and return a default value. If input is invalid, throw an error with a clear message.”

  2. Add try-catch blocks. Wrap API calls, database queries, and anything that can fail. Handle the error.

  3. Test failure scenarios. Disconnect the internet. Provide invalid input. Shut down the database. Does your code handle it gracefully?

  4. Add logging. Log errors. Log unexpected states. Log data as it flows through your system. When something breaks, the logs tell you why.

  5. Return sensible defaults. If something fails, what’s the safe fallback? Return empty, return null, show a user-friendly message?

Every line of code that can fail should have a handler for that failure. This isn’t optional.

Mistake 7: Not Reading the Code (Just Shipping Whatever AI Produces)

What it looks like: Claude generates code. You glance at it. It looks fine. You don’t actually read it. You ship it.

Later you discover it’s doing something you didn’t expect: calling an external API you don’t know about, using a library you didn’t approve, storing data you didn’t intend.

Why it happens: Reading code is slow. Understanding it is harder. You trust Claude. So you ship it.

The problem: You don’t understand what your own code is doing. When something breaks, you can’t debug it. When you need to modify it, you break it. You’re running code you don’t comprehend in production.

How to fix it:

  1. Read every file you ship. Every line. Actually read it.

  2. Understand the main flow. What happens when someone uses this? Trace the execution from input to output.

  3. Know your dependencies. What external libraries is this code using? Why? Are they necessary?

  4. Ask yourself: could I explain this to someone else? If you can’t explain it in 30 seconds, you don’t understand it well enough to ship it.

  5. Look for obvious red flags. Is it calling external APIs? Is it storing data somewhere? Is it making network requests? These need to be intentional.

  6. Comment confusing parts. If Claude generated something clever that you don’t immediately understand, add a comment explaining it. Future you will thank you.

The code you ship is your responsibility. If you can’t explain it, don’t ship it.

The Meta Pattern

Notice what these mistakes have in common?

They’re all about trusting AI more than yourself. You trust that the first output is right. You trust that Claude won’t introduce security issues. You trust that what you see is what you’re getting.

The real vibe coding skill isn’t in prompting. It’s in verification. You’re not asking “can Claude write code?” (yes). You’re asking “is this code right?” That requires reading, testing, and judgment.

The Recovery Pattern

If you’re already doing these things, here’s how to recover:

  1. Start with version control. Commit what you have. Make it the baseline.

  2. Add testing. Write tests for critical paths. Watch them fail on current code. Fix one at a time.

  3. Security audit. Use Snyk. Manual review. Fix the obvious vulnerabilities first.

  4. Read the code. Understand what’s actually happening. Document it.

  5. Iterate. Fix mistakes one by one. Test each fix. Ship.

It’s slower than shipping immediately. But slower-and-correct beats fast-and-broken.

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