Also known as: Least to most Prompting
Least-to-most prompting es un método de encadenamiento de prompts que descompone una tarea compleja en una secuencia de subproblemas más simples. Cada paso usa el resultado del paso anterior como contexto para el siguiente, de modo que el modelo avanza hacia una respuesta final mediante soluciones intermedias ordenadas. Es útil cuando una tarea es demasiado compleja para resolverse de forma fiable en un solo prompt.
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.