Why we should document AI-generated code like real developers do

I've been thinking about something.

We've gotten really good at using AI to generate working code, but we're not treating it like production code in terms of documentation.

Traditional developers spend significant time documenting their code because they know future them (or their teammates) will need to understand, modify, or debug it later. But with AI-generated code, we often just copy-paste and move on.

The problem:

You generate a perfect solution today.

Three weeks later, you need something similar.

You can't remember what the original code does or why you built it that way.

You end up re-prompting for the same solution.

What I've learned:

Taking time to document AI-generated code (what it does, why you needed it, how to modify it) creates a knowledge base you can actually build on. Instead of starting from scratch each time, you're accumulating reusable, understandable solutions.

Anyone else notice this pattern? How do you handle keeping context around the code AI writes for you?