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Related Questions
- What are some common pitfalls to avoid when using prompt engineering to bridge the knowledge gap between general-purpose and domain-specific LLMs?
- How can prompt engineering be used to improve the performance of domain-specific LLMs on out-of-domain tasks?
- What are some strategies for designing effective prompts that can elicit accurate and relevant responses from LLMs on domain-specific tasks?
- How can prompt engineering be used to mitigate the issue of knowledge drift in LLMs, where the model's knowledge becomes outdated or inaccurate over time?
- What are some best practices for evaluating the effectiveness of prompt engineering techniques in improving the performance of LLMs on domain-specific tasks?
- Can prompt engineering be used to transfer knowledge from a general-purpose LLM to a domain-specific LLM, and if so, how?
- What are some common challenges that arise when using prompt engineering to bridge the knowledge gap between general-purpose and domain-specific LLMs, and how can they be addressed?
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