{
  "id": "least-to-most-prompting",
  "code": "PTL-0030",
  "term": "Least-to-Most Prompting",
  "aliases": [
    "problem decomposition"
  ],
  "category": "reasoning",
  "definition": "Least-to-most prompting first asks the model to break a complex problem into simpler subproblems, then solves them in order, feeding each answer into the next.",
  "description": "Zhou et al. showed it generalizes to problems harder than those in the examples, a weakness of standard chain-of-thought, with strong results on compositional generalization benchmarks.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "chain-of-thought",
    "plan-and-solve-prompting",
    "self-ask",
    "prompt-chaining"
  ],
  "introduced": 2022,
  "sources": [
    {
      "title": "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models",
      "authors": "Zhou et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2205.10625"
    }
  ],
  "url": "https://protologue.com/t/least-to-most-prompting/",
  "citation": "Protologue. (2026). Least-to-Most Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0030). https://protologue.com/t/least-to-most-prompting/"
}