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Related Questions
- What techniques can prompt engineers use to anticipate and handle ambiguous or unclear input?
- How can prompt engineers design prompts that are robust to out-of-vocabulary words or domain-specific terminology?
- What strategies can prompt engineers employ to mitigate the impact of biased or misleading input on the model's output?
- How can prompt engineers evaluate the effectiveness of their prompts in handling edge cases and unexpected inputs?
- What role does data augmentation play in improving the robustness of prompts to edge cases and unexpected inputs?
- Can you provide examples of well-designed prompts that have successfully handled edge cases and unexpected inputs in real-world applications?
- What best practices can prompt engineers follow to ensure that their prompts are robust and effective in a variety of scenarios?
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