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Related Questions
- What are the key differences between prompt engineering and traditional machine learning model training in NLP?
- How does prompt engineering enable domain adaptation in NLP models, and what are its benefits?
- Can you provide examples of effective prompt engineering techniques for fine-tuning pre-trained language models for specific domains?
- What are some common challenges in prompt engineering, and how can they be addressed?
- How does prompt engineering impact the interpretability and transparency of NLP models?
- Can you explain the concept of prompt injection and its role in prompt engineering?
- What are some best practices for creating effective prompts for NLP models, and how can they be optimized for specific tasks?
- How does prompt engineering relate to other areas of NLP, such as data augmentation and transfer learning?
- Can you discuss the potential risks and limitations of prompt engineering, such as overfitting and bias?
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