This technique involves prompting the language model to provide specific instances or illustrations related to a topic. This approach helps clarify concepts and enhances the relevance and detail of the model's responses, ensuring that the output aligns closely with the user's expectations and needs.

This technique involves prompting the model to provide specific instances or illustrations related to a topic, which can help clarify concepts, enhance understanding, or generate more detailed and relevant responses. This approach encourages the model to draw from its training data to present concrete cases, making the information more relatable and easier to comprehend.

Perspective-shifting is a technique that involves prompting the language model to adopt different viewpoints or angles when generating responses. By encouraging the model to consider a situation from various perspectives, users can elicit a broader range of insights and ideas. This approach can be particularly useful in discussions that require empathy, critical thinking, or creative problem-solving. For example, a user might ask the model to respond to a question as if it were a child, an expert, or a member of a specific community, thereby enriching the conversation with diverse interpretations and understandings. Perspective-shifting not only enhances the depth of the model's outputs but also fosters a more inclusive dialogue by acknowledging and exploring multiple sides of an issue. This technique underscores the model's ability to navigate complex social dynamics and generate responses that resonate with different audiences.