The main trick is in how you build up it's context for the problem. What I do is think of it like a colleague I'm trying to explain the bug to: the overall structure is conversational, but I interleave both relevant source chunks and
detailed/
complete observational info from what I've observed about anomalous program behavior. I typically will send a first message building up context about the program/source, and then build up the narrative context for particular bug in second message. This sets it up with basically perfect context to infer the problem, and sets you up for easy reuse: you can back up, clear that second message and ask something else, reusing detailed program context given by the first message.
Using it on the architectural side you can follow a similar procedure but instead of describing a bug you're describing architectural revisions you've gone through, what your experience with each was, what your objectives with a potential refactor are, where your thinking's at as far as candidate reformulations, and so on. Then finish with a question that doesn't overly constrain the model; you might retry from that conversation/context point with a few variants, e.g.: "what are your thoughts on all this?" or "can you think of better primitives to express the system through?"
I think there are two key points to doing this effectively:
1) Give it full, detailed context with nothing superfluous, and express it within the narrative of your real world situation.
2) Be careful not to "over-prescribe" what it says back to you. They are very "genie-like" where it'll often give exactly what you ask for in a rather literal sense, in incredibly dumb-seeming ways if you're not careful.