Interesting ways to use General AI Devices for mistakes and lessons

I think general and devices is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. That should make it easier to tell whether the change is actually helping.

The point about general and devices is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. The original point — "I wanted to start a broader mistakes and lessons thread around General AI Devices. A useful angle is deployment, but it also connects with prompt design. Different tools and workf…" — gives a useful starting point for that comparison.