When to delete AI-generated code
The default fate of AI-generated code is to be kept, because deleting it feels like wasting the work that produced it. But the work is already spent, and keeping code has its own ongoing cost. Knowing when to delete generated code is part of healthy AI-assisted development, and the signals are usually clear once you look for them.
The sunk cost of generation
Generated code carries a quiet bias: it exists, so deleting it feels like undoing effort. But the tokens are already spent whether you keep the code or not. The only real question is forward-looking: does this code earn its ongoing maintenance cost? If not, the cheapest moment to remove it is now.
Signals that say delete
A few reliable indicators that generated code should go rather than stay:
- An abstraction with exactly one caller
- Handling for inputs that cannot reach this code
- A backwards-compatibility shim for a path nothing uses
- A second implementation of something that already exists
- A half-finished branch left "in case it is useful later"
Pruning is a first-class activity
Healthy codebases are pruned, not just grown. Treating deletion as normal work, rather than as an admission that the generation was wrong, keeps the system small enough to understand. The aim is a codebase where everything present is there for a reason that still holds.
Keeping code is a decision, not a default. Generated code should stay because it earns its place, not because deleting it feels like waste.
How Avorelo helps
Avorelo's scope enforcement and receipts make accumulation visible: every run records exactly what was added, so unused or speculative code does not disappear into the diff. Low-risk cleanups within configured limits can be applied automatically, and anything larger is surfaced for an explicit decision rather than kept by inertia.