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Related Questions
- What are the key concepts and relationships in a domain-specific knowledge graph that can inform prompt design?
- How can prompt engineers use entity recognition and relation extraction techniques to identify relevant nodes and edges in a knowledge graph?
- Can you explain the concept of 'knowledge graph embeddings' and how they can be used to optimize model training?
- What are some strategies for aligning prompt design with the structure and semantics of a domain-specific knowledge graph?
- How can prompt engineers use knowledge graph-based prompts to guide the model's attention mechanisms and improve its ability to reason over complex relationships?
- What role do graph neural networks play in leveraging the structure and semantics of a knowledge graph for prompt design and model training?
- Can you discuss the importance of evaluating the effectiveness of prompt design in relation to the underlying knowledge graph, and how to conduct such evaluations?
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