Vibe coding lets you rapidly create fragile software. You prompt, it generates code, you see lightning-fast progress. In a few hours, you have something that kind of works. You’re posting on LinkedIn about building a “complex app in 3 hours.”

But once you reach a certain level of complexity, reality hits. Edge cases break things, adding features breaks other parts, and debugging becomes a nightmare. Your motivation crashes and most projects die here, abandoned with half-finished dreams.

I’ve found that what separates shipped projects from abandoned ones is knowing how to rescue the project. This article shares what I learned from rescuing my own dead vibe-coded projects by getting back to the software principles I skipped. Many of these are practices we’ve known for decades, and they work just as well for AI-generated code.

Remedies: How to Rescue Your Project and Ship

1. Cut Scope

This is psychologically hard to admit, but this is often the first place you should start: your original scope was probably too large.

Vibe coding makes it incredibly easy to add features. You ask the AI for one thing, it builds it, you get excited and ask for another, then another. It feels good in the moment. But when you try to rescue all of that code into something shippable, it feels insurmountable. So much code, so many features, none of it quite working.

Boil it down to the minimum thing you want to ship that has value. Save everything else for a later release by putting it behind feature flags, disabling the code, or deleting it altogether if you think you’ll never ship it. Come to this realization quickly and focus on the important parts. Applying the latter steps in this article will be much easier with reduced scope.

A small shipped app is infinitely better than a massive dead one.

2. Establish Standards and Architecture

You’ve already built the app, and the AI probably produced a mess. One feature uses classes, another uses functions, files are in random folders. We’ll get to fixing that later. But first, let’s document the ideal architecture you want for this app. Think of this as defining your target state: how you wish the code was structured, not how it currently is.

What to document:

• Architecture: Choose a well-known pattern (Domain-Driven Design, MVC, unidirectional architectures, etc.) that the AI will be familiar with. Define your folder structure, module boundaries, data flow, and general naming conventions.
• Coding standards: Document patterns to follow, code smells to avoid, and how to handle common scenarios (errors, validation, logging, testing). These will likely evolve as you refactor the app. You may not know what you need to document until you start reviewing AI’s output with discipline.

I find this is actually one of the fun parts of using AI. Documenting how you want apps to be written is a great intellectual exercise that challenges you to think through questions you may have never considered before.

You can use AI to generate these docs initially, but review and understand them. I put mine in docs/architecture/ and docs/standards/ and reference them while working with AI. Continually iterate on these as you build.

An important insight: these documents are generic guidance for the AI, not specific to your individual project. They define how you want the AI to write code in general (your preferred patterns, architectures, and standards). This means you can copy these docs between projects and reuse them. I maintain a set of these for different project types like Mac apps, Python apps, etc. that I drop into new projects as starting templates.

When you have architecture and standards, you maintain understanding of your codebase. You know where things go, how they work, and why they’re structured the way they are. This is what keeps you motivated when things get hard.

3. Refactor with Your Standards and Architecture

Now that you have documented standards and architecture, it’s time to actually use them. This step is about refactoring your existing codebase to match the patterns and structure you’ve defined. This will get the app into a shape that you understand and that is hopefully more robust and easier to maintain going forward. This is prime candidate work for using AI.

Start with architecture, as that’s the most important piece. Reorganize your folder structure, establish clear module boundaries, and ensure data flows the way you’ve documented. Once the architectural foundation is solid, you can work on updating the code to follow your standards over time.

This is a piece you’ll be particularly proud of when you’re done, transforming chaos into something intentional and well-structured.

4. Build Feedback Loops

One of the reasons your project may not be shippable is that the AI was building without a way to validate how it was doing. The AI needs to know when its changes break things, and you need to know you’re not introducing regressions. Feedback during software development is not a new concept but matters even more with AI-generated code.

How to provide feedback:

• Validation Against Standards – After creating a small feature or fixing a bug, have the AI validate its work against the standards and architecture docs you created earlier. This ensures it followed your patterns and conventions. AI tools often have hooks that can automate this validation step.
• Testing – Use unit tests for individual functions, integration tests for how components work together, and end-to-end tests for complete user workflows. Tests catch regressions and verify the system actually works as intended.
• Logging and error handling – AI can read log output as it’s running things to see what’s actually happening. Log key operations, state changes, and decision points. AI often generates happy-path code, so ensure every operation that can fail has explicit error handling with clear messages.

5. Review All AI-Generated Code

You’re probably not going to review all the existing code. That ship has sailed. But mark a line in the sand: all new code created during the rescue will be reviewed, every commit and every change. This is part of your strategy for making things better.

Why? Because in nearly every AI-generated change, you’ll find something you want different. Without review, you’ll miss these issues and accumulate more technical debt. Just as importantly, reviewing keeps you connected to your architecture and what’s actually in your codebase.

Conclusion: Ship It

You don’t need to abandon these projects. By cutting scope, establishing standards, refactoring to match them, building feedback loops, and reviewing code, you can rescue that dead vibe-coded app and actually ship it. And on your next project, you can apply these more disciplined approaches from the start and build something maintainable.