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Related Questions
- What are the key factors to consider when crafting a prompt for LLMs to ensure they can ground their responses in reality?
- How can prompt designers utilize specific contextual information to aid LLMs in making more accurate and relevant predictions?
- What role do linguistic features like specificity, ambiguity, and semantic clarity play in promoting effective grounding in LLMs?
- How can prompt designers strike a balance between providing sufficient context and avoiding over-constraint, which can impede the LLM's ability to generalize?
- What are some common pitfalls to avoid when designing prompts for LLMs, and how can designers mitigate these issues to improve grounding?
- Can you provide some examples of well-designed prompts that have been used successfully in real-world applications?
- How do LLMs' cognitive biases and limitations impact prompt design, and what strategies can be employed to minimize these effects and promote effective grounding?
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