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Related Questions
- How can prompt engineering techniques, such as prompt chaining and prompt refinement, be used to elicit more flexible and adaptable text from language models?
- What are some strategies for designing prompts that can handle out-of-vocabulary words or domain-specific terminology?
- Can you provide examples of how to use natural language generation techniques, such as paraphrasing and text summarization, to create more flexible and adaptable prompts?
- How can language models be trained to respond to open-ended prompts and generate more creative and innovative text?
- What are the key differences between explicit and implicit prompts, and how can they be used to elicit more flexible and adaptable text?
- Can you discuss the role of context and background knowledge in prompt design, and how it affects the flexibility and adaptability of generated text?
- What are some best practices for evaluating the effectiveness of prompts in generating flexible and adaptable text, and how can they be improved over time?
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