least-to-most prompting

A form of prompt chaining that divides complex problems into an ordered set of simpler problems. For example, here's a least-to-most prompting strategy for a certain problem: 1. Divide a complex problem into an ordered list of simpler sub-problems. For this example, assume it is three sub-problems. 2. Prompt 1: Ask the LLM to solve the first sub-problem. The LLM returns Response 1. 3. Prompt 2: Integrate all or part of Response 1 into the prompt to solve the second sub-problem. The LLM returns Response 2. 4. Prompt 3: Integrate all or part of Response 2 into the prompt to solve the third sub-problem. The LLM's response to Prompt 3 is the "final" answer to the initial complex problem. Note that each step depends on the solution to the preceding step. Contrast with tree-of-thought prompting.

Practitioners refer to least-to-most prompting when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.