You can call them planning if you want or pre-planning. But I would encourage you to play with the API version of your model of choice to see exactly what this looks like. It’s kind of like a human’s internal monologue: “got an email from my boss asking to write unit tests for the analytics API. First I have to look at the implementation to know how exactly it actually functions, then write out what kinds of tests make sense, then implement the tests. I should write a TODO list of these steps.”

It is essentially a way to expand the prompt further. You can achieve the same exact thing by turning off the “thinking” feature and just being more detailed and step by step in your prompt but this is faster.

My guess is that the next evolution of this will be models that do an edit or review step after to catch if any of the constraints were broken. But best I can tell a reasoning model can be approximated by doing two passes of a non-reasoning model: first pass you give it the user prompt with instructions that boil down to “make sense of this prompt and formulate a plan” and the second pass you give it the original prompt, the plan, and an explanation that the plan is to implement the original prompt using the plan.