AI-generated codebase rescue
The problem with an AI-generated codebase is usually not that every line is wrong. It is that conventions, boundaries and tests were never made explicit. A rescue engagement turns repeated symptoms into a map of dependencies, risks and safe refactoring milestones.
Run the project, trace important user journeys, inspect API boundaries and reproduce the most costly errors.
Fix authentication, data validation, error handling, types and deployment issues that can cause data loss or outages.
Add tests around the business rules that matter and replace unclear modules only when the next product goal requires it.
Generated code can be perfectly useful and still lack consistent decisions about where validation lives, who can call an API, how errors are reported and which module owns a business rule. Those gaps compound as more prompts add more files.
The first audit should identify the boundaries that matter to the product. It should not turn every unfamiliar style choice into a rewrite requirement.
Teams usually ask for help after a small change produces an unrelated regression or after the application becomes too unpredictable to extend. The useful response is to connect the symptom to the missing engineering control.
Start with data access, authentication, payments and destructive actions because a defect there can affect users or money. Then improve observability and tests around those paths. Cosmetic cleanup can wait until the code is safe to change.
Each milestone should leave the application running and make the next change easier to verify. That is safer than pausing the product for a wholesale cleanup with no intermediate proof.
A useful first commit
The first safe commit often adds reproduction steps, one regression test and one narrow fix. It proves the new engineer can work with the repository without widening the risk.
No. Audit the running system first. Keep code that expresses the product and replace boundaries that create measurable security, testing, deployment or maintenance risk.
Yes. Start with a few business-critical flows and regression cases around the current failure modes. Tests should protect the next refactoring milestone rather than attempt to cover every file at once.
Usually yes. The repository, deployment logs, reproducible errors and expected user behaviour are more important than the original prompt history, although prompt context can help explain why a generated decision was made.
