Full Walkthrough: Workflow for AI Coding — Matt Pocock

Software engineering fundamentals remain crucial when integrating AI into development workflows. LLMs operate within a "smart zone" of limited context, requiring developers to break large tasks into small, manageable chunks to avoid performance degradation. Effective AI-assisted coding relies on achieving a shared design concept through iterative "grilling" sessions, where the AI acts as a partner to resolve dependencies and clarify requirements. Rather than relying on specs-to-code movements, developers should maintain control through vertical slices—tracer bullets of functionality that cross system layers—and test-driven development to ensure high-quality feedback loops. By treating the AI as a partner in a human-in-the-loop process, developers can delegate implementation to automated agents while retaining architectural oversight, ultimately improving code quality through deep, testable modules rather than shallow, fragmented ones.