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Related Questions
- What techniques do prompt engineers use to ensure LLMs can generalize across different domains while still being domain-specific?
- How do prompt engineers strike a balance between the need for domain-specific knowledge and the need for generalizable language understanding in LLMs?
- Can you explain the importance of domain adaptation in LLMs and how prompt engineers address this challenge?
- What are the implications of domain specificity on the interpretability of LLM outputs, and how do prompt engineers mitigate these issues?
- How do prompt engineers determine the optimal level of domain specificity for a given LLM application?
- What role does transfer learning play in balancing domain specificity and generalizability in LLMs, and how do prompt engineers leverage it?
- Can you discuss the relationship between prompt engineering and the concept of few-shot learning, and how they intersect in terms of domain specificity and generalizability?
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