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Simple example of problem my team ran across.

The distance between "dairy creamer" and "non-dairy creamer" is too small. So an embedding for one will rank high for the other as well, even though they mean precisely opposite things. For example, the embedding for "dairy free creamer" will result in a low distance from both of the concepts such that you cannot really apply a reasonable threshold.

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But in a larger frame, of "things tightly associated with coffee", they mean something extremely close. Whether these things are opposite from each other, or virtually identical, is a function of your point of view; or, in this context, the generally-meaningful level of discourse.

At scale, I expect having dairy vs non-dairy distance be very small is the more accurate representation of intent.

Of course, I also expect them to be very close and that's the problem with purely relying on embeddings and distance where, in this case, the two things mean entirely opposite preferences on the same topic.

(I think maybe why we sometimes see AI generated search overviews give certain types of really bad answers because the underlying embedding search is returning "semantically similar" results)

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