Best practices for today's technology

The reality of being a developer is that we often spend more time fixing code that actually writing new code. Debugging is such a core and time-consuming part of software development, it’s no surprise that most developers now use some fomr of artificial intelligence, like IDE-embedded LLMs like GitHub Copilot, Cursor or other such products. It’s not always plain-sailing, however, with any users in our community saying that while they find AI to be hugely useful, becoming too reliant on it is a fool’s errand, and developers need to be careful not to fall into AI’s traps. In this article, we’ll help you navigate its limitations and avoid common pitfalls when relying on AI for debugging your code. AI Debugging: What It Can (and Can’t) Do AI-driven debugging tools leverage large datasets and pattern recognition to suggest fixes for bugs, optimise performance, and even detect vulnerabilities. While these are hugely useful, it’s important to remember AI does not “understand” code in the way ...