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Related Questions
- What are the key concepts and relationships in ontologies and knowledge graphs that can be used to inform LLM prompts?
- How can ontologies and knowledge graphs be used to provide context and meaning to LLM prompts, improving model performance?
- Can you provide examples of how ontologies and knowledge graphs can be used to create more accurate and relevant LLM prompts?
- How do ontologies and knowledge graphs enable LLMs to better understand and interpret user intent, leading to improved performance?
- What are some best practices for integrating ontologies and knowledge graphs into LLM prompt engineering to achieve optimal results?
- Can you discuss the role of ontologies and knowledge graphs in addressing common challenges in LLM prompt engineering, such as ambiguity and uncertainty?
- How can ontologies and knowledge graphs be used to create more nuanced and context-dependent LLM prompts, taking into account user background and preferences?
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