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Chain-of-thought prompting (asking the model to 'think step by step') dramatically improves reasoning accuracy.

For complex reasoning, you can often trigger CoT by simply appending 'Let's think step by step.' to your prompt. The model will then show its reasoning process before the final answer.

For mathematical or logical reasoning problems, adding the phrase 'Let's think step by step' before the LLM generates its solution significantly improves accuracy.

A more advanced technique where the LLM is prompted to generate both reasoning traces and actions to take to solve a problem, often interacting with external tools.

Ask the LLM to think about its thinking process: 'Before answering, consider what approach would be most effective for this problem and explain your reasoning strategy.'