Practical Strategies for Controlled AI-Assisted Coding

AI coding tools now support code generation, error explanation, refactoring, implementation planning and debugging, but programmers lack a widely established practical framework for deciding how much control to retain. This lecture presents a framework spanning different levels of human direction and agent delegation. It distinguishes stages of planning, coding, testing, debugging, verification and documentation to help researchers, students and developers choose appropriate assistance for each task.

The aim is to preserve understanding, reliability and control while using language models productively. The lecture also examines responsibilities and risks involving correctness, security, licensing, data protection and scientific accountability.