The Vibe Coder's Guide to Debugging

One builder spent five days debugging an authentication issue that kept breaking in production. The culprit? An upstream GitHub repo with an action that was systematically overwriting their work.

The kicker: They spent $200 upgrading to faster AI models to help debug. Claude, ChatGPT, and Cursor's Sonnet all missed it. The AI was "gaslighting" them with confident solutions that didn't address the real problem.

Another builder faced users unable to upload large video files. PostHog analytics, Replit AI, and Claude all gave 10+ different diagnoses. Nothing worked.

The breakthrough came from a public call for help in a vibe coding community. Someone who's had a ton of engineering experience immediately spotted it: Replit was uploading videos to the wrong destination—not to Google Cloud Storage as intended.

Here's what these stories reveal: Vibe coding is not just about code. It's all about understanding the full system and how information flows between each piece.

As one builder put it: "My limitation was actually I didn't understand how Edge functions worked. I didn't understand how the internet worked, so I was asking the AI to do something it was very happy to try. But the reality is I was asking the wrong thing."

The solution? Before debugging, ask the AI to teach you the concept first—in a separate conversation: "If you were to teach a developer the best way to do this, what would you explain?"

Learn about buckets, learn about S3, learn about different routing options. Suddenly you realize: "Oh, my problem was actually I was trying to force a huge flow of water through a tiny little hole."

Step 1: Define What You're Actually Seeing

Don't say "it's broken." Describe the error precisely:
• What happens?
• What did you expect?
• When does it occur (every time or occasionally)?
• Can you explain what's happening?

Even if you can't explain the solution, describing the problem accurately is half the battle.

Step 2: Understand the Type of Problem

Problems fall into categories that AI handles differently:




Step 3: Strategic Debugging Moves

Tightly limit scope
Start at the simplest place. Then expand gradually over 4-5 sessions. You'll have to "slap the hand of the AI" to keep it focused. Don't let it run off fixing tangential problems.

Use logging, not guessing
Tell the AI: "Don't try to solve this. Identify suspicious areas and add debug logs. Print the state, print the variables, print what's going in and out of functions."

When you reproduce the bug, you'll see the data causing it. Then share those logs with the AI, it can usually figure it out from there.

Test constantly
Every small change, check if it works. Don't make multiple changes in a row. When something breaks, you need to know exactly which change caused it.

Go back to the database
Copy the actual rows of data your app is processing. Paste them into the AI conversation. Now it can see the fields and values and work backwards: "Why is this showing the wrong value?"

Use Socratic debate
This is a cheat code. Tell the AI to have a Socratic debate—either with you or against itself—about what the problem could be. This forces methodical, step-by-step thinking and teaches you how the AI reasons about your code.

Step 4: Know When to Rewind

You'll hit a point where you've tried five rounds of fixes and nothing works. Everything you added is either worthless or making it worse.

Don't go back blindly. Instead:
• Document what you tried (in a separate notes file)
• Document what didn't work and why
• Use this as a reference: "We already tried X, it failed because Y"
• Revert to your last working state
• Try a different approach with fresh context

As one builder noted: "Better to have shorter conversations where you get to something important, make that observation, restart the context, and move to the next problem."

Step 5: Be Wary of Context Limits

Allowing the AI to fill up its context window, then compact it, then fill it up again makes it "dumb."

Have focused conversations. When you solve something or learn something important, start fresh. Don't drag 10,000 tokens of debugging history into every new attempt.

Cheat Code #1: Playwright

If you're in a cloud environment or using Cursor, use Playwright (an MCP tool owned by Microsoft). It lets you run through your application with a browser and see errors in real-time. Many builders use browser-based debugging like this.

Cheat Code #2: Separate Documentation Agent

Keep a separate repo for documentation - your PRD, markdown notes, bug logs, CSVs.

Have a second AI agent window open just for that repo. Use it for planning, reflection, and strategizing: "Here's the bug. Let's think through approaches."

When you have something solid, prepare it as a prompt for your main coding agent.

Why this works: You don't need to give your entire codebase context for every conversation. The code is about logic and thinking. Separate the two.

One builder thought they were doing everything right by building 1,000 tests while building their app. But nothing was testing a critical hook that later caused major issues.

It took four days to strip down the test framework to essentials and understand what actually needed testing.

Key insight: Tests written in code are only as effective as the scope they cover.

• Unit tests check specific functions: "Does calculateStreak() return the right value?"
• Integration tests check combinations: "Does the file watcher trigger the upload function correctly?"
• End-to-end tests are often manual: You test the full user flow yourself

Test-driven development (writing tests before code) can be efficient but requires time investment. Reactive testing (write tests when bugs appear) is more practical for solo builders.

If you're using Edge functions, Vercel deployments, Supabase, or any external services—tell the AI everything that's not in your direct control.

The AI assumes your problem is in the code. But if you mention "this runs in a Vercel Edge function," it might immediately recognize: "Oh, there's a timeout limit that's causing your issue."

Give it the full stack: where it deploys, what storage you use, what services are involved. The AI knows the internet—it can often spot service-specific limitations you'd never think of.

You see an error. You've built an events app, and events appear in the timeline on the wrong date, but it's not happening consistently. The AI suggests filtering out specific timestamps. Problem solved, right?

Wrong. You're not solving the root cause, you're just hiding the symptom.

Push back: "Why is this happening in the first place? I don't want to add a filter. Fix the actual problem."

Another builder's example: Videos showed "unavailable" errors during upload. The AI suggested a workaround to hide those videos, without actually solving the issue.

Every builder in the session echoed this: "I know so much more now than when I started. Leaps and bounds."

As you build, you'll realize your early code was naive. You'll want to go back and update patterns everywhere, optimize approaches you've learned.

One builder realized their test framework was missing critical logic tests. Another learned the difference between SQL and NoSQL requires different mental models for data.

This is normal. Software development is iterative. You learn by doing. The AI helps you move faster, but the learning compounds over time.

Debugging as a vibe coder isn't about becoming a developer. It's about:

• Understanding systems (beyond just the code)
• Using AI strategically (it's a thinking partner, not an oracle)
• Knowing when to ask humans (communities spot patterns AI misses)
• Debugging methodically (logging > guessing)
• Managing context (fresh sessions vs dragging history)

The AI won't always save you. But with the right approach, you can maintain ownership and control over your projects—even when they scale beyond what any one person can hold in their head.

That's what separates vibe coders who ship from those who get stuck.

What's your worst debugging nightmare? What finally solved it? Share your war stories in the vibe coding community—someone else is probably stuck on the exact same problem right now. The pattern you spotted after days of frustration could save another builder hours of headache.