A prototype built over a weekend in Lovable or Cursor today looks more convincing than some products with a five-year history. The difficulties begin later, when the demo is turned into a business, and the first hundred paying users arrive. Let’s analyze what breaks first in such systems and how to put them in order without a complete rewrite.
The Demo Passes, Production Doesn’t
A tool like Replit or GitHub Copilot solves precisely the task assigned at the moment. It doesn’t keep the model of the entire system in mind, doesn’t remember that similar logic was already written two folders up, and doesn’t ask about peak loads. The code looks like it’s working because it actually works on the data and in the scenario where it was tested. The moment the environment changes, hidden assumptions start to surface. A larger dataset, concurrent users, or integration with external services often exposes edge cases that never appeared during the initial prototype stage.
That’s exactly why Vibe Coding Cleanup transformed from an exotic concept into a distinct market sector in a couple of years. This isn’t about cosmetics: engineers disassemble generated code and catch hallucinated logic, security permission holes, and places where the system will stop handling the load. Such work starts with an audit, not refactoring, since without a risk map it’s unclear what to fix first.
The scale of the phenomenon is recorded by independent measurements. GitClear research on AI code quality showed that the number of duplicated blocks grew from 40.3 to 73.0 per million changed lines, while the share of moved code, the main sign of refactoring, dropped to 3.8%. In other words, there is more code, but less order in it.
Debt Nobody Ordered
Technical debt in vibe coding is structured differently than in regular legacy. It accumulates not over years of compromises but over a few weeks of confident generations, while externally the product remains quite neat. A typical set that surfaces at the very first serious breakdown:
- The same logic duplicated in several places with minor discrepancies;
- Keys and tokens hardcoded directly into the repository;
- Lack of tests and documentation, making any edit turn into a lottery.
Developer sentiment aligns with the numbers. A Stack Overflow survey among 49 thousand specialists recorded that 66% encounter solutions that are almost right, but not quite, and 45% waste time debugging generated code. Although trust in tools is dropping, people haven’t started using them any less. However, it’s precisely the gap between the perception of speed and the real cost of support that ultimately determines the product’s fate.
Where the Breakdown Begins
Auditing a generated codebase differs from a classic review because you have to read everything back-to-back: there is no documentation, and comments here more often describe intention rather than actual behavior. Engineers go through several layers of the system and build a risk map from their findings.
| Audit area | What usually surfaces | Why it matters at scale |
| Access control | Endpoints without permission checks | Data exposure grows with every new user role |
| Data layer | Queries written per screen, no indexes | Response times collapse under concurrent load |
| Error handling | Failures swallowed by broad catch blocks | Incidents stay invisible until users report them |
| Dependencies | Unpinned packages and unused imports | Every update turns into an unplanned risk |
| Test coverage | No regression suite around core flows | Each release becomes a manual verification round |
Given that each of these zones is uncovered in a day or two of work, a full-fledged audit rarely takes more than two weeks. Then the actual cleanup begins, and that stretches on for months. Conversely, a complete rewrite is rarely required: most of the prototype is usually fit for purpose if you build proper architecture around it.
When the Cost of an Error Isn’t Measured Only in Money
In healthcare, fintech, and legal tech, not only users come to the product but auditors too. HIPAA requirements for encryption and access logging, corporate customer expectations for SOC 2, and industry data storage norms — none of this is added on top of a ready prototype in a week. Besides, generative tools don’t distinguish between a shopping list and a medical record: they set access permissions equally carelessly.
This becomes especially clear during the first enterprise deal, when a client sends a multi-page security questionnaire, and the team opens its own code with a flashlight for the first time and realizes it has no answers. For many teams, the first serious audit also becomes the first time the system is documented in a structured way. That documentation often proves just as valuable as the code changes themselves during future development.
Final Thoughts
A working demo is proof of concept, not a finished product, and the difference between them is measured in months of engineering work. The earlier an honest audit is conducted, the cheaper it costs to bring the system into order. Thus, the question isn’t whether to fix the consequences of vibe coding but whether to do it before the first major client or after.
Related: What Tasks Is Generative AI Actually Good For? A Practical Guide
| Disclaimer: This article was written by a guest contributor and reflects the author’s own views and experience with vibe coding and AI-generated software. The information is provided for general informational purposes and should not replace professional technical, security, or compliance advice. Always review and test AI-generated code carefully before using it in a production environment. |
