That's the value prop of large language models (and here, of OpenAI's LLM): because it's been trained on some "somehow sufficiently large" corpus of data, it ha

That's the value prop of large language models (and here, of OpenAI's LLM): because it's been trained on some "somehow sufficiently large" corpus of data, it has internalized a lot of real world concepts, in a "superficial yet oddly good enough" way. Good enough for it to have embeddings for "dancing" and "tango" that will be fairly close. And if you really need to, you can also fine-tune your LLM or do few shot learning to further customise your embeddings to your dataset.