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Related Questions
- What are some common pitfalls in designing healthcare-related prompts for LLMs, and how can they be avoided?
- How can prompt engineering techniques, such as template-based prompting, improve the accuracy and reliability of LLMs in healthcare?
- What are some strategies for evaluating the interpretability of LLMs in healthcare, and how can prompt engineering contribute to this process?
- Can you provide examples of how prompt engineering can be used to improve the explainability of LLMs in healthcare, such as by generating more transparent and concise output?
- How can prompt engineering help address the issue of concept drift in LLMs, which can occur when the model is applied to new, unseen data in a healthcare setting?
- What role can prompt engineering play in ensuring that LLMs in healthcare are fair and unbiased, and how can this be measured?
- How can prompt engineering be used to develop more robust and generalizable LLMs in healthcare, which can handle a wide range of clinical scenarios and patient populations?
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