In the rapidly evolving world of technology, AI is becoming an integral part of our coding practices. But with great power comes great responsibility — and in this case, that responsibility is thorough documentation.

When I first started integrating AI into my coding projects, I was blown away by the speed and efficiency it brought to the table. However, I quickly realised that if I didn’t document my processes meticulously, I’d be left with a mess of code that neither I nor anyone else could decipher.

AI can generate code snippets, optimise algorithms, and even debug issues at lightning speed. But it can’t explain why it did what it did. That’s where documentation comes in. It’s not just about commenting your code; it’s about creating a narrative that explains the thought process behind your coding decisions. This is especially important when AI is involved, as it can suggest solutions that might not be immediately obvious to a human coder.

Moreover, documentation serves as a safeguard against the potential pitfalls of AI-generated code. AI models can sometimes produce biased or flawed outputs, and without proper documentation, these issues can go unnoticed and become embedded in your project. By documenting your process, you’re creating a paper trail that allows you — or anyone else who works on the project — to trace back any problems and rectify them.

But let’s not forget the human element. When you’re working in a team, good documentation is a sign of respect for your colleagues. It shows that you value their time and intelligence, and that you’re committed to creating a collaborative environment. Plus, it can save you from those awkward moments when you’re asked to explain a piece of code you wrote months ago, and you realise you have no idea what past you was thinking.

So, if you’re coding with AI, don’t let the speed and efficiency it offers lull you into complacency. Document like your project depends on it — because it does. Trust me, your future self will thank you.