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Related Questions
- What are some strategies for identifying and mitigating the impact of out-of-distribution data on model performance in prompt engineering?
- How can prompt engineers design robust prompts that handle edge cases and unexpected inputs?
- What are some techniques for detecting and handling ambiguous or unclear prompts that may lead to out-of-distribution responses?
- Can you discuss the importance of data curation and validation in ensuring that training data includes representative edge cases?
- How can prompt engineers balance the need for generalizability with the risk of overfitting to edge cases?
- What are some best practices for testing and evaluating a model's performance on edge cases and out-of-distribution data?
- Can you explain the concept of 'adversarial testing' in the context of prompt engineering and its role in identifying edge cases?
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