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Related Questions
- What are the key differences between active learning and traditional machine learning approaches in prompt engineering?
- How can prompt engineers use human evaluation and feedback to refine their prompts and improve model performance?
- What are some common pitfalls to avoid when implementing active learning in prompt engineering, and how can they be mitigated?
- Can you provide examples of successful applications of active learning and iterative refinement in prompt engineering, and what lessons can be learned from them?
- How can prompt engineers balance the trade-off between collecting more data and refining their prompts through active learning, to achieve optimal results?
- What role does human judgment play in active learning and iterative refinement, and how can it be integrated with machine learning algorithms?
- How can prompt engineers measure the effectiveness of their active learning and iterative refinement approaches, and what metrics should they use to evaluate their success?
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