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Related Questions
- What are the key considerations for prompt engineers when designing prompts for large language models to ensure optimal performance?
- How can prompt engineers use techniques such as paraphrasing and rephrasing to increase the flexibility of natural language prompts?
- What are the trade-offs between specificity and flexibility in prompt engineering, and how can engineers balance them?
- How can prompt engineers use context-dependent prompts to achieve a balance between specificity and flexibility?
- What role do ontologies and knowledge graphs play in enabling more flexible and specific natural language prompts?
- How can prompt engineers use active learning and feedback loops to refine and adapt prompts over time?
- What are some best practices for prompt engineers to follow when designing prompts for multi-task learning and transfer learning scenarios?
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