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Related Questions
- What happens if a language model is given a vague or open-ended prompt with multiple possible answers?
- How can specificity in prompts impact the accuracy of the model's output, especially in domains where slight variations in interpretation matter, such as in law, medicine, or finance?
- Why is specificity in prompts particularly crucial in tasks that involve handling specific formats, dates, and data, like financial forecasts or historical research?
- Can you give examples of how clear and detailed prompts can prevent language model hallucinations, misinterpretations, and biased or incorrect responses?
- How can model designers use prompt engineering to encourage desired behavior, like specifying confidence intervals or required format
- Can specificity in prompts help alleviate issues such as ambiguity and contextual loss when dealing with language limitations and nuances
- How does specifying scope in prompts, e.g. specific time period or a specific domain impact model robustness and maintainability for the task of
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