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Related Questions
- What are the key concepts in a specific domain that can be used to create high-quality prompts for LLMs?
- How do prompt engineers use their knowledge of the domain to identify relevant entities, relationships, and concepts that can be used to fine-tune LLMs?
- Can you explain the role of domain knowledge in creating informative and specific prompts that elicit accurate and relevant responses from LLMs?
- How do prompt engineers adapt their domain knowledge to different LLM architectures and training data to achieve optimal performance?
- What are some common pitfalls that prompt engineers may encounter when leveraging domain knowledge to fine-tune LLMs, and how can they be avoided?
- Can you provide examples of how domain knowledge can be used to create more effective prompts for LLMs in specific domains, such as medicine or law?
- How do prompt engineers measure the effectiveness of their domain knowledge in fine-tuning LLMs, and what metrics can be used to evaluate their performance?
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