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Related Questions
- What are the key performance metrics to use when evaluating the effectiveness of prompt engineering strategies?
- How can we quantify the impact of prompt engineering on a model's ability to generalize to out-of-distribution data?
- What are some common pitfalls to avoid when designing and evaluating prompt engineering strategies for improving out-of-distribution performance?
- Can you explain the relationship between prompt engineering and model interpretability, and how does it affect out-of-distribution performance?
- How can we use techniques like A/B testing and experimentation to validate the effectiveness of prompt engineering strategies?
- What role do you think data augmentation and data curation play in improving a model's out-of-distribution performance, and how can prompt engineering be used in conjunction with these techniques?
- Are there any specific prompt engineering techniques that are more effective for certain types of out-of-distribution scenarios, such as handling unknown entities or rare events?
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