When working with o1/o3 models, I always have this feeling that I'm leaving a lot on the table with my prompting. Creating a long sequence of prompts for regular LLMs is good practice. This is because you don't want to overload what an LLM can process (or it'll lead to hallucinations). But Large Reasoning Models (LRMs) are different.

The benefit of packing a lot (but not too much, there's always a balance) is that you are trying to uncover connections that you otherwise can't see via an explicit sequential approach. This brings up the question then, what are the best ways to construct a composite prompt?

I've laid up some ideas in my book on how to compose these. intuitionmachine.gumroad.com/l/o1/bt1r8d5

@IntuitMachine I've found that breaking down complex prompts into smaller, manageable chunks can help uncover those connections.