AI‑Assisted Development: How to Get the Code You Want Without the Hidden Technical Debt

Series Introduction, Purpose, and Overview 💡 A Note Before You Begin I know the topic is wide and it may feel daunting to read all nine series posts in one go. But I am 100% sure this will boost your knowledge and make your day‑to‑day life as a software developer easier. Treat this as a reference—read one post per week, or jump straight to the mistake that bit you yesterday. The goal is to help you use AI tools with confidence, not overwhelm you. Introduction & Background AI coding assistants—from GitHub Copilot and Cursor to ChatGPT and Claude—have become ubiquitous in software development. They accelerate prototyping, automate boilerplate, and offer instant debugging suggestions. But with great power comes great responsibility. As a senior software architect and engineering productivity researcher, I've observed a recurring pattern: developers—both junior and senior—fall into predictable traps when using AI tools. These mistakes range from subtle context omissions that lead to i