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Related Questions
- How can I leverage ontologies and knowledge graphs to inform LLM prompts and improve model performance?
- What are some strategies for using domain-specific terminology and nomenclature in LLM prompts to reduce ambiguity and increase accuracy?
- Can you provide examples of how to use implicit cues, such as context and relationships, to guide LLMs in generating more relevant and informative responses?
- How can I use explicit cues, such as keywords and phrases, to specify the desired output and control the LLM's behavior?
- What are some best practices for creating LLM prompts that balance specificity and generality to accommodate varying levels of domain knowledge and uncertainty?
- How can I use LLMs to identify and incorporate missing or outdated domain knowledge into the training data?
- What are some techniques for using LLMs to reason and infer domain-specific knowledge from incomplete or noisy data sources?
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