Building with Lovable, Bolt, or v0 is fast. Debugging when something breaks? Most people just keep re-prompting and wonder why it gets worse.
The problem isn’t the AI. It’s that you’re throwing prompts at symptoms instead of the actual error. A Redditor in r/PromptEngineering just dropped a structured debugging playbook that flips this whole workflow around, and it’s one of the more practical guides I’ve seen for vibecoded apps.
Here’s the full breakdown.
Quick Start
What you’ll learn: how to catch bugs before they blow up, find the exact error when something breaks, and use a structured fix waterfall to resolve issues fast. What you need: any vibecoding tool (Lovable, Bolt, v0, Prettiflow) and a basic git setup.
The Old Way vs. the Right Way
The old way: something breaks, you describe it vaguely to the AI, get a confident-sounding fix, it breaks something else, repeat until everything is on fire. You end up three hours deep into a problem that started as a two-line bug.
The right way: find the actual error string first. The post’s author calls it your “debugging currency.” Everything else follows from it.
Before It Even Breaks
Two habits that save you hours later:
- Use your own app as you build. Click through every feature. If you won’t test it, the AI definitely won’t. Treat it like a user who’s trying to break things, not just happy-path it.
- Watch for red squiggles in your editor. Red means critical error. Yellow means warning. Don’t ignore them hoping they go away; a yellow today is often a red tomorrow at 2am when you’re trying to ship.
🔍 When It Breaks: Find the Error First
Two places to look:
- Terminal (where you run
npm run dev) , server-side errors live here - Browser console (Cmd+Shift+I on Chrome) , client-side errors live here
Copy the exact error message. All of it. That string is what you bring to the AI, not “it’s broken.” The difference between “TypeError: Cannot read properties of undefined” and “my button doesn’t work” is the difference between a 30-second fix and a 30-minute spiral.
The Fix Waterfall (Do This in Order)
The original poster’s core method is a five-step fix waterfall. Work through it top to bottom before skipping ahead:
- Commit to git when it works. This is your time machine. Most vibecoding tools have a rollback button, but it only goes back one step. Git lets you return to any point you explicitly saved. Build this habit now, not after you need it.
- Add more logs. If the error isn’t obvious, tell the AI: “add console.log statements throughout this function.” Make the invisible visible before you try to fix anything. For example, if a form submission silently fails, logging the request payload before it hits the API will usually tell you exactly what’s missing.
- Paste the exact error into the AI. Full error, copied whole. “Fix this.” The author says most bugs die right here, and honestly that tracks. If you paraphrase the error, you’re already filtering out information the model needs.
- Google it. Stack Overflow, Reddit, docs. If the AI fails after 2-3 attempts, it’s usually a known issue with a known fix that just isn’t in its context. This step feels old-school but it works, especially for dependency conflicts or framework-specific quirks.
- Revert and restart. Go back to your last working commit. Try a different model or rewrite your prompt with more detail. Not failure, just the process.
The Sneaky One: Behavioral Bugs
Crashes are obvious. Logic bugs are not.
When something works sometimes but not always, that’s not a crash; it’s a behavioral bug. The fix: describe the exact scenario. “When I do X, Y disappears, but only if Z was already done first.” Specificity is everything. Vague bug reports produce confident-sounding wrong answers. A good rule of thumb: if you can’t write the bug as a reproducible step-by-step sequence, you don’t understand it well enough to fix it yet. Slow down, reproduce it twice, then write the prompt.
The Real Bottleneck
The models are genuinely good at debugging now. The bottleneck is almost always the context you give them, or don’t. Better error reporting plus solid git hygiene, and you’ll spend way less time rebuilding things that were working yesterday.
One thing the community added worth noting: after a big bug is fixed, add end-to-end tests to catch regressions. It’s surprisingly easy to fix the same bug twice when there’s no safety net underneath.
What to Do Next
Pick one habit from this playbook and start today. If you’re not committing to git regularly, start there , it’s the single highest-leverage change on the list. Then bring the exact error string to your next debugging session instead of describing the general vibe of what’s wrong.
The full discussion is in r/PromptEngineering, with some solid community additions in the comments. Worth a look if you’re building seriously with any of the vibecoding tools!
Frequently Asked Questions
Q: What do I do if the AI keeps giving me wrong fix attempts?
After 2, 3 failed attempts, the AI’s context gets contaminated with its own wrong hypotheses. Start a fresh chat with just the error and relevant code snippet, or revert to your last working commit and try a different approach. This resets the conversation and prevents the AI from going deeper down the wrong rabbit hole.
Q: How do I describe a bug so the AI understands it faster?
Structure it as three parts: (1) exact state before the bug, (2) the exact action that triggers it, (3) what you expected vs. what you got. This minimal repro format dramatically cuts back-and-forth and helps the AI pinpoint the issue faster than vague descriptions like “it breaks sometimes.”
Q: How do I prevent the same bug from reappearing when I add new features?
Add end-to-end tests that cover the exact scenario where the bug occurred. When the AI adds new features, these tests act as a safety net, they’ll catch it if the AI accidentally reintroduces the bug or breaks something related.
Q: What if my app seems to work but I suspect hidden edge case bugs?
Write automated tests to actively hunt for them using Jest, Playwright, or bash scripts, test race conditions, rapid-fire clicks, unusual input sequences, and state transitions. Don’t just read the code and guess; make the AI prove the bugs exist with actual test output, then fix and re-test to confirm.
How to ACTUALLY debug your vibecoded apps.
by u/julyvibecodes in PromptEngineering