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Related Questions
- How can designers structure prompts to elicit nuanced and contextualized language outputs from LLMs?
- What role do semantic categories play in ensuring LLMs ground language understanding, and how can they be effectively leveraged in prompts?
- How do prompts using situated cognition impact LLM performance, and what best practices can developers employ to improve this technique?
- What methods can developers use to minimize ambiguity and maximise coherence in prompt formulation?
- In what ways do context-driven approaches to prompt engineering promote richer language understanding in LLMs, and what best practices have been shown to be effective?
- How do prompt structures impact LLM ability to discern relationships and nuances, and what optimal structures can achieve the desired level of comprehension?
- What heuristics or methods for evaluating and refining prompt designs can enhance the efficacy of LLM language understanding and prevent model overfitting?
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