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What is Chain-of-Thought (CoT) prompting

Chain-of-Thought (CoT) prompting involves guiding the model to articulate a step-by-step reasoning process when answering a question or solving a problem. Instead of providing a direct answer, the model is encouraged to “think out loud,” detailing the intermediate steps that lead to the final conclusion.

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How to implement Chain-of-Thought prompting

  1. Instruct the model to show its work. Explicitly ask the model to provide step-by-step reasoning.
  2. Provide examples with reasoning steps. Demonstrate the desired approach by including examples that show the reasoning process.
  3. Use prompts that encourage explanation. Incorporate phrases that prompt the model to elaborate.
  4. Leverage few-shot learning with chain-of-thought. Combine CoT prompting with few-shot learning by providing examples that include reasoning steps.

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Examples

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Why use Chain-of-Thought prompting

  • Improves reasoning accuracy: Helps the model handle complex queries by breaking them down into manageable steps.
  • Enhances transparency: Provides insight into how the model arrives at an answer, which can be valuable for verification and trust.
  • Facilitates error detection: Easier to identify and correct mistakes in the reasoning process.
  • Encourages detailed responses: Generates richer and more informative outputs.

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Tips for effective Chain-of-Thought prompting

  • Be explicit and direct in your request.
  • Provide examples to demonstrate the process.
  • Use open-ended questions to encourage elaboration.
  • Maintain clarity and focus to avoid ambiguity.
  • Limit the scope for complex topics.