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chain-of-thought

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Elicit step-by-step reasoning from language models to boost accuracy on math, logic, and multi-step problems.

skills/chain-of-thought/SKILL.md

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## Overview

Chain-of-thought (CoT) prompting gets language models to reason through problems
step by step before answering, instead of jumping straight to a final answer. The
mechanism is straightforward: by generating intermediate reasoning tokens, the
model gives itself more computation per problem and a scratchpad to hold partial
results. On arithmetic, logic puzzles, and multi-hop questions, this reliably
produces large accuracy gains over direct answering — it's one of the most
replicated findings in prompting research.

The two flavors are zero-shot CoT ("Let's think step by step" appended to the
prompt) and few-shot CoT (demonstrations showing worked reasoning). Few-shot CoT
with well-crafted demonstrations generally outperforms zero-shot, but zero-shot is
a strong, nearly-free baseline. The modern ecosystem extends the idea: