Chain-of-thought prompting is a technique where you instruct a language model to reason step by step before giving a final answer. Instead of asking 'What is 17% of 340?' directly, you say 'Think through this step by step.' The model then reasons through intermediate steps before arriving at an answer. The final answer is more accurate because the model caught and corrected its own reasoning mid-process. This technique works because LLMs generate text sequentially. When forced to articulate intermediate reasoning, the model effectively proofreads its own logic before committing to a conclusion. Research shows chain-of-thought prompting greatly improves performance on math, logic, and multi-step reasoning tasks. Zero-shot chain-of-thought simply adds 'Let's think step by step' to a prompt. Few-shot chain-of-thought provides example reasoning chains before the actual question. Modern reasoning models like o1 and o3 apply chain-of-thought internally before producing output, which is why they are slower but more accurate on complex problems.
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