Prompt Engineering
Use Chain-of-Thought prompting
By encouraging the model to generate intermediate reasoning steps before arriving at a final answer, you can achieve more accurate and insightful responses.
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.
How to implement Chain-of-Thought prompting
- Instruct the model to show its work. Explicitly ask the model to provide step-by-step reasoning.
- Provide examples with reasoning steps. Demonstrate the desired approach by including examples that show the reasoning process.
- Use prompts that encourage explanation. Incorporate phrases that prompt the model to elaborate.
- Leverage few-shot learning with chain-of-thought. Combine CoT prompting with few-shot learning by providing examples that include reasoning steps.
Examples
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.
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.