[DHolzer]

> Totally disagree here, using embeddings is much more reliable / robust, I wouldn't put much stock in LLM output, too much going on

I think both ways can be the preferable option, depending on how well the embedding space represents the text - and that is mostly dependet on the specific use case and model combination.

So if the embedding space does not correctly project required nuance, then it's often a viable option to get the top_n results and do the rest by utilizing the llm + validation calls.

But i do agree with you, i would always like to work with embeddings rather than some llm output. I think it would be such a great thing to have rock solid embedding space where one would not even consider to look at token predictor models.