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Related Questions
- What are some common evaluation metrics used to measure the performance of language models in responding to user prompts?
- How do prompt engineers ensure that their prompts are well-defined and unambiguous to avoid confusing the language model?
- What are some strategies for testing the robustness of language models in handling out-of-distribution inputs and edge cases?
- Can you explain the concept of prompt engineering and its role in fine-tuning language models for specific tasks?
- How do prompt engineers validate the fairness and bias of language models in their responses to different user prompts?
- What are some best practices for documenting and sharing prompt engineering knowledge and expertise within a team or organization?
- How do prompt engineers balance the trade-off between providing specific and general information in their prompts to achieve optimal model performance?
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