How can we track AI vs human-generated code in our projects?

Here's a cheeky idea: why not have Q prefix all the names it generates? It sounds goofy, but it could work! Also, consider having developers write a comment with the original prompt at the top of their files for better tracking of AI input.

Honestly, why does it really matter who generated the code? The key factor is who approved the changes and who's responsible for the pull request. We tackled this at my last job by focusing on integration and approval rather than tracing the origin of the code.

Tracking AI-generated vs. human-written code is quite tricky. There's really no perfect way to tell the difference. It's much like trying to determine if code was copied from Stack Overflow and slightly modified. If an AI tool created 80% of the app and you tweaked it, who gets credit there? That's a big question!

It's hard to effectively track which code is AI-generated; it's a slippery slope that kind of resembles the old 'lines of code' metric. I suggest assessing the expected effort for a task and comparing it with how quickly a dev can complete it—that might give you better efficiency insights.

I think management is missing the point here. Instead of focusing on who wrote the code, they should look at existing engineering metrics like cycle time and defect density to see if using AI tools is changing productivity over time.

You might want to reach out to Amazon Q Developer directly. They might have insights or features that enable recognition of its own generated code which would help with your tracking efforts.