{
  "id": "complexity-based-prompting",
  "code": "PTL-0115",
  "term": "Complexity-Based Prompting",
  "aliases": [],
  "category": "reasoning",
  "definition": "Complexity-based prompting selects chain-of-thought exemplars that contain more reasoning steps, and at inference votes only among the sampled reasoning chains with the most steps.",
  "description": "The method rests on the finding that demonstrations with longer, more complex reasoning improve multi-step reasoning more than simpler ones. It combines exemplar selection with a variant of self-consistency in which votes from the most complex chains count.",
  "example": null,
  "broader": [
    "chain-of-thought"
  ],
  "narrower": [],
  "related": [
    "exemplar-selection",
    "self-consistency",
    "auto-cot"
  ],
  "introduced": 2022,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Complexity-Based Prompting for Multi-Step Reasoning",
      "authors": "Fu et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2210.00720"
    }
  ],
  "url": "https://protologue.com/t/complexity-based-prompting/",
  "citation": "Protologue. (2026). Complexity-Based Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0115). https://protologue.com/t/complexity-based-prompting/"
}