Chain-of-Thought Prompting
Also called CoT, step-by-step reasoning.
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?
A: He starts with 5. Two cans of 3 is 6. 5 + 6 = 11. The answer is 11.Sources
- Wei et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.
Cite this entry
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/
BibTeX
@misc{protologue_chain_of_thought,
title = {Chain-of-Thought Prompting},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0024},
url = {https://protologue.com/t/chain-of-thought/}
}