AI-assisted development changes the cost of producing code. It does not change the cost of owning it. A generated implementation still creates dependencies, security assumptions, maintenance work and future migration costs.
The useful question is not whether AI makes a developer faster. It is whether the team can review, test and maintain what it produces.
This guide sets out a repeatable workflow for using coding assistants without allowing the codebase to become a collection of unrelated generated decisions.
Start with constraints, not a prompt
Before implementation, record the problem, acceptance criteria and boundaries.
A small task brief should state:
- the user-visible behaviour that must change;
- the files or modules that own that behaviour;
- existing conventions that must be preserved;
- interfaces that must not change;
- required tests and verification commands;
- explicit exclusions.
Prefer observable acceptance criteria. "Saving an item survives an app restart" is testable. "Make persistence robust" is not.
Ask the assistant to inspect the relevant code and report its understanding before editing. Correct misunderstandings before they become patches.
Keep changes reviewable
Break work into changes that can be understood independently. A feature implementation should not casually include dependency upgrades, formatting across the repository, a new state manager and unrelated refactoring.
Require a reason for each new dependency or abstraction. The default should be the existing platform and project conventions.
A useful review sequence is:
- Confirm the change solves the stated problem.
- Read the complete diff, including generated files.
- Check ownership, data flow and error handling.
- Identify new permissions, network calls and persisted data.
- Run focused tests, then the repository's full verification command.
- Inspect the running behaviour where the change affects a user interface.
A passing test suite is evidence, not proof that the change is correct.
Treat generated code as an untrusted contribution
Review code for the same failure modes as human-written code:
- secrets or credentials exposed to client bundles or logs;
- unvalidated input crossing trust boundaries;
- broad permissions and unnecessary network access;
- swallowed errors or false success states;
- unsafe shell construction;
- unbounded retries, loops or resource consumption;
- missing cancellation and cleanup;
- dependencies that are abandoned or disproportionate.
Do not let an assistant approve or merge its own work without an independent review gate.
Preserve architectural consistency
Document decisions that affect future work: module boundaries, API contracts, persistence formats, authentication assumptions and operational limits.
Do not create a large architecture document for a trivial change. A short decision record is enough when the choice is consequential.
If generated code introduces a pattern that conflicts with the project, either adapt it now or explain why the existing pattern should change. Leaving two competing approaches in place is deferred maintenance.
Use a definition of done
A practical definition of done for AI-assisted changes is:
- acceptance criteria are met;
- the diff contains no unrelated changes;
- types, lint and tests pass;
- security-sensitive paths have been reviewed;
- documentation reflects changed behaviour;
- a human can explain the implementation;
- deployment and rollback are understood.
Track defects and rework rather than claiming a fixed productivity multiplier. Results depend on task complexity, codebase quality, model capability and review effort.
Further reading
The principle is straightforward: use AI to reduce the cost of implementation, not to remove engineering responsibility.