As teams increasingly incorporate AI-assisted code generation into their development workflows, the long-term maintenance of these systems—specifically managing the hidden technical debt generated by automated suggestions—is becoming a significant concern for lead engineers. Beyond traditional version control, what specific strategies have you found effective to ensure that AI-generated modules remain maintainable and adhere to established standards over the life of a project?
