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Related Questions
- Can you explain how contextualization in prompt engineering helps to reduce bias in machine learning models by providing more accurate and nuanced context for the model to learn from?
- What are the potential consequences of neglecting contextualization in prompt engineering, and how might it lead to biased or inaccurate model outputs?
- How does contextualization help mitigate the problem of 'out of distribution' errors, where the model performs poorly on data that is different from the training data?
- Can you discuss the importance of including contextual information in prompts to avoid relying solely on surface-level data, which can be biased or misleading?
- What are some best practices for incorporating contextualization into prompt engineering to ensure that machine learning models are more accurate and less biased?
- How does contextualization affect the interpretability of machine learning models, and can it help to identify potential biases or errors in the model's decision-making process?
- Can you explain the relationship between contextualization and the concept of 'cultural competence' in machine learning, and how it can help to reduce bias in models that interact with diverse populations?
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