Chain-of-Thought (CoT): Definition and Examples
Chain-of-Thought (CoT) pushes the model to decompose its reasoning into explicit steps before the final answer, significantly improving quality on logic tasks.
Full definition
CoT, introduced by Google Research in 2022, is a major breakthrough. The idea: ask the model to think step by step.
Why does it work? LLMs generate token by token. Thinking out loud allows each step to serve as context for the next, reducing logic errors.
CoT is effective for math, logical reasoning, multi-criteria analysis, and planning. On math benchmarks, +20-40% performance.
Two variants: explicit CoT ('Reason step by step' — zero-shot CoT) and CoT by examples (few-shot with full reasoning).
Etymology
Formalized in 'Chain-of-Thought Prompting Elicits Reasoning in LLMs' (Wei et al., Google Brain, 2022). The analogy: a chain of thought where each link strengthens the next.
Concrete examples
Math problem
A store offers 20% off an item priced at €85, then a loyalty discount of €5. Final price?
Reason step by step.
Complex decision analysis
Compare 3 job offers step by step (salary, location, prospects, culture).
A: €55K Paris startup | B: €48K Lyon large group | C: €60K remote US scale-up
Analyze each criterion then recommend.
Code debugging
This Python code has a bug. Analyze the flow line by line, identify the error, propose a fix.
Practical usage
Add 'Reason step by step' to your complex prompts. For calculations, ask to show each operation.
Related concepts
FAQ
Does CoT work with all models?
Does CoT consume more tokens?
CoT vs Tree-of-Thought?
See also
How to use this prompt
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