Least-to-Most Prompting

Least-to-most prompting is a technique that teaches a model to first solve the easiest subproblem, use that solution to tackle the next slightly harder one, and build up incrementally to the full problem. This scaffolded approach exploits the model's ability to condition on correct earlier steps, dramatically improving performance on compositional reasoning tasks where jumping directly to the answer is error-prone.