Technical Distinctions

From a technical standpoint, the divergence between VKGs and LLMs becomes even more pronounced:

– Training: VKGs rely on training embedding models to understand relationships among structured and unstructured data (e.g., electronic health records, scientific literature). LLMs, in contrast, require vast corpora of natural text to learn patterns probabilistically.
– Computation: VKGs are optimized for efficient graph traversal and vector similarity operations, allowing real-time performance with minimal compute resources. LLMs often require intensive computation, especially during inference with large models.
– Error Control: VKGs are deterministic and inherently resistant to hallucination. LLMs, by their nature, may generate fluent but false information—an unacceptable risk in medical contexts.