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Related Questions
- Can you explain how designers use contextual clues to refine user intent and avoid ambiguity when crafting prompts?
- What role does nuance and subtlety play in effective prompt-writing, and how can designers balance brevity and clarity?
- How can designers use tacit knowledge and real-world experience to inform their writing of prompts and make them more meaningful?
- In what ways does the language and tone of the prompt impact the quality and relevance of the response generated by the model?
- Can you describe the relationship between prompt- engineering and the concept of 'knowledge retrieval' and how it affects the efficiency of the model?
- How can designers use empathy and understanding to create more inclusive and respectful prompts, especially in cases where they are addressing sensitive or minority groups?
- What techniques can designers employ to 'debug' ambiguous or unclear prompts and recover from unintended consequences in the responses generated by the model?
- Can you discuss the importance of actively engaging with the model through iterative and recursive prompts versus relying solely on initial training data?
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