On the other hand, your post may contain a good idea: L=instruct_LLM("provide a list of synonyms and periphrases of terms T within context C", T, C); then iter(`grep l in L`). One NN query and a `grep` collection. But again, if one wanted to order the results, it is either through a dumb crierion or through another LLM query - but this could make it extremely costly (requiring either a huge context or a quadratic number of ordering queries).

And, the above `grep` based procedure would remain keyword based and not semantic based, which means that the user must know that it will not be based on comprehension but on the possible results that keyword matching can yield.

(BTW: you made me realize - I had to take "time off" for over half a year... I am sure I missed a lot.)

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Edit: for clarity: for "full text search" we remain on the interpretation of "searching for literal substrings" - and whether plain user provided keywords list or LLM enriched list based on the former, and whether more or less successful, it remains a syntactic search quite distinct from a semantic one. Having an LLM enrich the original keywords list can be a good idea, but the possibility of misses remains when compared to a properly working semantic search.

You can see it in action if you watch the “chain of thought” text when using coding agents.