{
  "id": "auto-cot",
  "code": "PTL-0027",
  "term": "Automatic Chain-of-Thought",
  "aliases": [
    "Auto-CoT"
  ],
  "category": "reasoning",
  "definition": "Automatic chain-of-thought (Auto-CoT) builds chain-of-thought demonstrations without manual writing, by clustering questions for diversity and generating a reasoning chain for a representative of each cluster with zero-shot CoT.",
  "description": "Diversity across clusters limits the damage from mistakes in any single generated chain, and the approach matched manually written CoT exemplars on several benchmarks.",
  "example": null,
  "broader": [
    "chain-of-thought"
  ],
  "narrower": [],
  "related": [
    "zero-shot-chain-of-thought",
    "exemplar-selection"
  ],
  "introduced": 2022,
  "sources": [
    {
      "title": "Automatic Chain of Thought Prompting in Large Language Models",
      "authors": "Zhang et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2210.03493"
    }
  ],
  "url": "https://protologue.com/t/auto-cot/",
  "citation": "Protologue. (2026). Automatic Chain-of-Thought. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0027). https://protologue.com/t/auto-cot/"
}