What does research say about: Chain-of-thought prompting improves complex reasoning accuracy?

Adding 'Let's think step by step' improves accuracy on GSM8K math benchmarks from 17.7% to 78.7% — a 4.4x improvement on multi-step reasoning tasks. (Source: Wei et al., 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models', Google Research, 2022). Without chain-of-thought, models attempt to produce answers in a single leap, failing on problems requiring intermediate steps.

Chain-of-thought prompting improves complex reasoning…

Context & Methodology

Without chain-of-thought, models attempt to produce answers in a single leap, failing on problems requiring intermediate steps.

Applicable Use Cases

workflow

Applies To

openaianthropicgoogle

Primary Impact

quality

Confidence Level

High

Platform Status

Built

Implementation Effort

low

Recommendation

Execution Priority

Dependencies & Conflicts

Conflicts with:

econ-003

Put This Evidence to Work

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