I spend a lot of time thinking about the gap between what code actually does and what people understand about the code. It's a real problem, especially once you have more than a handful of people touching the same repository.
Early in my work with AI-assisted development, I noticed that teams were using these tools to write code faster, but they were drowning in undocumented systems. You'd get a feature shipped, then three weeks later someone would ask where the webhook handling logic lives, and suddenly you're the bottleneck again. The AI made us productive at writing, but we stayed stuck at understanding. I started using documentation tools alongside my AI workflow—right now I'm using ShipDocs to keep our codebase documented without that huge manual overhead—but the real lesson was that you need both layers working together. Code generation speeds you up; good documentation keeps you from getting lost.
The workflow that actually works is this: when you're adding something new with AI assistance, you're not just thinking about whether the code functions. You're thinking about whether the next person touching this will know why it's structured this way. That changes how you prompt the AI, what you ask for, and what you push back on. I ask for more detailed comments. I request architectural decisions get written down. Then the documentation layer captures all that context in a way that's searchable and accessible to the whole team.
It sounds like overhead, but it's not. It's the difference between shipping fast and shipping in a way your team can actually maintain.
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