What it takes to fix a vibe-coded app

We hear a version of the same story often. A founder or a small team used an AI tool to build something fast, it found real users, and now it needs to keep working with more users and data than it was built for. The question is always the same: do we rewrite this, or can it be saved. Almost always, it can be saved, and choosing a rewrite by instinct usually costs more than it saves.

Resist the urge to start over

Rewriting feels safer than fixing, because new code has no unknown bugs in it yet. That feeling is misleading. A rewrite throws away the working parts along with the broken ones, takes far longer than people estimate, and a rewritten system carries its own new risks until it has been tested by real use the way the original eventually was. Reserve a full rewrite for the case where the architecture is fundamentally wrong for what the product needs to do next, not for the case where the code is simply unreviewed.

Start with an honest audit

Before changing anything, understand what you actually have. Run static analysis and dependency scanning to find known issues automatically. Trace the flows that matter most to the business: signup, payment, anything that touches sensitive data, and read them closely using the review approach in how to review AI-generated code. The goal of this step is a real list of what is broken, ranked by how much it matters, not a vague feeling that "the code needs work."

This step is where teams either save themselves months of wasted effort or waste them. Fixing things in the order you happen to notice them, rather than in the order they matter, is how a fix project continues for months without the risk actually going down.

Fix in risk order

Once you have the list, work it in order of what a failure would cost you, not in the order that is easiest or most visible. Authentication and access control come first, because a flaw there can expose everything else. Anything touching payments or user data comes next, for the same reason. Core business logic that the product depends on for its main purpose comes after that. Cosmetic issues, unclear naming, and minor inefficiencies come last, because they cost you convenience, not trust.

This ordering matters because a team under pressure to "clean up the code" often starts with what is easiest to fix, which is rarely what is most dangerous to leave broken. Protect first the parts that would cause the most damage.

Add tests as you go, not after

Vibe-coded software usually has few or no tests, since tests are the first thing skipped when moving fast with a prompt. As you fix each risky area, add a test that would catch a regression in that specific flow. Do not try to reach full coverage before releasing again; that goal is both unrealistic and not where the value is. Cover the flows that would hurt the business most if they silently broke, and let that coverage grow naturally as you touch more of the code.

This turns the fix process into something that adds value over time. Every area you secure gets tests that protect it, so future changes, by a human or by AI, are less likely to break it silently.

Keep the parts that work

Not everything needs your attention. Code that is low-risk, works correctly, and is unlikely to change often is fine to leave alone, even if it is not written the way you would have written it by hand. Spending review time on code that carries little risk and rarely changes is effort better spent on the flows that matter. The goal is a codebase that is safe where it needs to be safe, not a codebase that is uniformly polished.

When to bring in outside help

Teams usually look for outside help at one of two points: when the audit itself feels too large to do without experienced people, or when the fix work competes directly with the feature work the business needs and there simply are not enough senior hours to do both. Either is a reasonable reason to bring in a team that does this regularly. What to look for in that team is covered in how to evaluate an AI development partner.

Where Reveneau fits

This is the work Reveneau is built around: taking a fast AI-built prototype that proved an idea and making it something a real business can run on, without losing the speed that built it. If you have something that needs this kind of risk-ordered repair, talk to us and we will give you an honest assessment of what it actually needs, which is not always a rewrite and not always a small patch. The main guide covers where this fits alongside review and governance.