Comment by baobabKoodaa

The comment I was responding to describes Jev's significance as being specifically about embeddings:

> Jev is basically the embeddings side of an LLM

This is how the paper you are referencing is describing the "key contribution" as it relates to embeddings:

> Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific features

And now you are claiming that the author of the paper created:

> "generalized" version of same

It's a bit hard to guess what you are trying to say, but if I were to steelman your argument, I would guess that you mean: training a model with a vocabulary that has some tokens representing things like "OPTION_A", "OPTION_B" is in your mind the same thing as "generalized version of sales-specific features in embeddings"? Is this what you were trying to say?