{
  "id": "chain-of-thought",
  "code": "PTL-0024",
  "term": "Chain-of-Thought Prompting",
  "aliases": [
    "CoT",
    "step-by-step reasoning"
  ],
  "category": "reasoning",
  "definition": "Chain-of-thought (CoT) prompting elicits a sequence of intermediate reasoning steps from a language model before its final answer, which improves performance on multi-step arithmetic, commonsense, and symbolic reasoning tasks.",
  "description": "Wei et al. introduced CoT by including worked examples with step-by-step reasoning in a few-shot prompt, and found the benefit emerged mainly in large models. It is the root of a large family of techniques, including zero-shot CoT, self-consistency, and tree of thoughts, and of the extended reasoning trained into modern reasoning models.",
  "example": "Q: Roger has 5 balls. He buys 2 cans of 3 balls each. How many balls does he have?\nA: He starts with 5. Two cans of 3 is 6. 5 + 6 = 11. The answer is 11.\n",
  "broader": [],
  "narrower": [
    "zero-shot-chain-of-thought",
    "auto-cot",
    "self-consistency",
    "tree-of-thoughts",
    "contrastive-chain-of-thought",
    "thread-of-thought"
  ],
  "related": [
    "scratchpad",
    "reasoning-model",
    "unfaithful-chain-of-thought"
  ],
  "introduced": 2022,
  "sources": [
    {
      "title": "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models",
      "authors": "Wei et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2201.11903"
    }
  ],
  "url": "https://protologue.com/t/chain-of-thought/",
  "citation": "Protologue. (2026). Chain-of-Thought Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0024). https://protologue.com/t/chain-of-thought/"
}