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Term

Chain-of-Thought

Chain-of-Thought (CoT) is a prompting technique that asks the model to spell out its reasoning in intermediate steps — boosting accuracy on multi-step tasks.

Chain-of-Thought — explained in more detail

The simplest trigger is the instruction “think step by step” or a handful of examples in which the solution is derived step by step. Instead of guessing an answer directly, the model produces a sequence of partial reasoning steps in between — much like working through a maths problem or a logic puzzle on paper. On larger models this noticeably improves accuracy on reasoning benchmarks, because the model no longer has to compress multi-step thinking into a single forward pass.

Example / Practical context

For a word problem like “Anna has 3 apples, gives 1 away, and buys 2 more — how many does she have?”, a zero-shot prompt sometimes produces the wrong number, while a CoT prompt with “step by step” yields the intermediate arithmetic and reliably arrives at 4. Reasoning-oriented models such as GPT-5 or Claude Opus internalise CoT mechanics (often called “extended thinking” or “reasoning tokens”) so that users rarely need to add the trigger explicitly.

Few-shot prompting shows input/output examples without exposing the reasoning path — CoT exposes the path on purpose. Self-Consistency is an extension: multiple CoT paths are generated and the most frequent final answer wins. Tree of Thoughts goes further and explores multiple solution trees in parallel.

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