When working with AI for software engineering assistance, I use it mainly to do three things -

1. Do piddly algorithm type stuff that I've done 1000x times and isn't complicated. (Could take or leave this, often more work than just doing it from scratch)

2. Pasting in gigantic error messages or log files to help diagnose what's going wrong. (HIGHLY recommend.)

3. Give it high level general requirements for a problem, and discuss POTENTIAL strategies instead of actually asking it to solve the problem. This usually allows me to dig down and come up with a good plan for whatever I'm doing quickly. (This is where real value is for me, personally.)

This allows me to quickly zero in on a solution, but more importantly, it helps me zero in strategically too with less trial and error. It let's me have an in-person whiteboard meeting (as I can paste images/text to discuss too) where I've got someone else to bounce ideas off of.

I love it.

Same 3 is the only use case I've found that works well enough. But I'll still usually take a look on google / reddit / stackoverflow / books first just because the information is more reliable.

But it's usually an iterative process, I find pattern A and B on google, I'll ask the LLM and it gives A, B and C. I'll google a bit more about C. Find out C isn't real. Go back and try other people commenting on it on reddit, go back to the LLM to sniff out BS, so on and so on.