Applying classic software engineering principles to AI coding tools can dramatically improve output quality. Seven key practices are covered: using AI as a planning partner to build PRDs, breaking work into user stories with acceptance criteria, implementing vertical slices (tracer bullets) to avoid overengineering, providing feedback loops (tests, linters, browser checks), separating test-writing from implementation using subagents, enforcing module boundaries with thin public interfaces, and running automated code reviews against defined conventions. The core argument is that AI works best when guided by the same discipline and structure that makes human-written code maintainable.
Table of contents
1. Dig into the Requirement2. Break It Down into Stories and Acceptance Criteria3. Vertical Slice First4. Tighter Feedback Loops5. Separate Writing Tests From Implementation6. Be Clear on your Boundaries (Modular Architecture)7. Review, Review, ReviewIn SummarySort: