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Related Questions
- How can I craft prompts that avoid leading questions and encourage the model to provide accurate and relevant information?
- What strategies can be employed to prevent the model from generating responses that are overly reliant on context or previous conversations?
- How can I design prompts that effectively avoid the 'hall of mirrors' effect and encourage the model to generate novel and informative responses?
- What are some techniques for making prompts more specific and focused to reduce the likelihood of the model generating off-topic or irrelevant responses?
- Can you suggest ways to limit the model's reliance on common patterns or tropes in the training data to minimize the 'hall of mirrors' effect?
- How can I use linguistic and semantic cues to guide the model towards generating more accurate and relevant responses?
- What are some best practices for testing and refining prompts to ensure they elicit the desired responses and avoid the 'hall of mirrors' effect?
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