It really comes mostly down to being able to concisely and eloquently define what you want done. It also is important to understand what the default tendencies and biases of the model are so you know where to lean in a little. Occasionally you need to provide reference material.
The capabilities have grown dramatically in the last 6 months.
I have an advantage because I have been building LLM powered products so I know mechanically what they are and are not good with. For example.. want it to wire up an API with 250+ endpoints with a harness? You better create (or have it create) a way to cluster and audit coverage.
Generally the failures I hear often with "advanced" programmers are things like algorithmic complexity, concurrency, etc.. and these models can do this stuff given the right motivation/context. You just need to understand what "assumptions" the model it making and know when you need to be explicit.
Actually one thing most people don't understand is they try to say "Do (A), Don't do (B)", etc. Defining granular behavior which is fundamentally a brittle way to interact with the models.
Far more effective is defining the persona and motivation for the agent. This creates the baseline behavior profile for the model in that context.
Not "don't make race conditions", more like "You value and appreciate elegant concurrent code."
