Codebase clarity
Untangling an AI-generated codebase.
When every fix creates another surprise, the first job is to make the system understandable again.
The challenge
The code grows faster than the understanding.
Repeated prompts can leave similar logic in several places, conflicting assumptions and features that depend on accidental behavior. A rewrite may look attractive, but it also risks discarding working decisions that nobody has documented. First, find the part of the system that is making change difficult.
The approach
Understand the behavior before changing the code.
Use a real failure or difficult change as the entry point. Build enough understanding to improve that path without turning the entire codebase into a new project.
Follow one behavior.
Trace the relevant inputs, state changes and outputs. Separate observed behavior from what the code appears intended to do.
Protect what matters.
Add focused checks around the behavior that must survive. Make uncertainty visible before moving responsibilities between modules.
Simplify one boundary.
Remove a specific duplication or dependency, then verify the same behavior still works. Leave a clearer place for the next change.
What to verify
A codebase that is easier to reason about.
The useful evidence is a change that can be explained, reviewed and verified without relying on another round of guesswork.
- Key behavior has a clear owner.
- The changed path has meaningful checks.
- The next change has a smaller, understandable scope.