[heisenburgzero]

In my own experience, if the problem is not solvable by a LLM. No amount of prompt "engineering" will really help. Only way to solve it would be by partially solving it (breaking down to sub-tasks / examples) and let it run its miles.

I'll love to be wrong though. Please share if anyone has a different experience.

[TheCowboy]

I think part of the skill in using LLMs is getting a sense for how to effectively break problems down, and also getting a sense of when and when not to do it. The article also mentions this.

I think we'll also see ways of restructuring, organizing, and commenting code to improve interaction with LLMs. And also expect LLMs to get better at doing this, and maybe suggesting ways for programmers to break problems down that it is struggling with.

[stets]

I think the intent of prompt engineering is to get better solutions quicker, in formats you want. But yeah, ideally the model just "knows" and you don't have to engineer your question