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An AI-assisted codebase becomes difficult to maintain for familiar reasons: unclear responsibilities, duplicated rules, inconsistent conventions, and changes that nobody fully reviewed. Generating code quickly can accelerate those problems if the project has no shared structure.
Take a product with discounts in its checkout, invoice preview, and renewal screen. If each screen calculates the price independently, a policy change can produce three different totals. The problem is ownership of the pricing rule, regardless of who or what wrote the functions.
Give important rules one home
Identify the business rules that must stay consistent. Examples include permissions, prices, eligibility, and status transitions. Put those rules behind clear application interfaces that the relevant screens and jobs reuse.
Before adding a helper, search the existing project for similar behavior. Give the assistant the established implementation and explain where the new feature belongs. Without that context, it may create another reasonable-looking abstraction that quietly disagrees with the first one.
Keep project guidance short and concrete
Document how to run the application, where major responsibilities live, how to execute important checks, and which conventions new changes should follow. Include a representative example when a pattern is not obvious.
Guidance should reflect the current codebase. A long instruction file describing an abandoned framework can mislead both developers and assistants. Update it when a meaningful architectural decision changes, and distinguish requirements from suggestions.
Control dependencies and generated files
Review every new runtime dependency. Consider whether the project already has an adequate solution, how the dependency is maintained, and what compatibility or packaging work it adds. Keep version constraints and lockfiles consistent with the ecosystem’s normal practices.
Separate editable source from build output and temporary experiments. Make the build reproducible from documented inputs. A future maintainer should be able to identify which files to edit and which files will be regenerated.
Refactor with observable boundaries
When improving a tangled module, first establish the behavior that must remain intact. Add meaningful checks for important rules where coverage is missing. Move one responsibility at a time, run the relevant checks, and inspect the resulting diff.
Avoid asking for a broad cleanup while also changing customer behavior. Separating those tasks makes regressions easier to trace. If the existing behavior is wrong, document the correction explicitly instead of hiding it inside a refactor.
Make each change explainable
- Keep pull requests or commits focused on one purpose.
- Explain the rule or user outcome being changed.
- Remove dead experiments once their useful parts are integrated.
- Keep setup instructions and public interfaces current.
- Review error handling and logs alongside successful behavior.
For a solo creator, this discipline is a favor to the person who will fix the product six months later. That person may be you, working with a different assistant and little memory of the original conversation.
Use a repeatable AI coding workflow to keep new work manageable. Maintenance becomes easier when the code, project notes, and tests all describe the same application and each important rule has an identifiable owner.