They're much more useful when an LLM stands between them and users - because LLMs can (re)process much more of them, and much faster, than any human could ever hope to.
One way (and one use case) of looking at it is, LLM agents with access ("tools") to semantic search[0] are basically a search engine that understands the text it's searching through... and then can do a hundred different things with it. I found myself writing better notes at work for this very reason - because I know the LLM can see them, and can do anything from surfacing obscure insights from the past, to writing code to solve an issue I documented earlier.
It makes notes no longer be write-only.
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[0] - Which, incidentally, is itself enabled by LLM embeddings.
One way (and one use case) of looking at it is, LLM agents with access ("tools") to semantic search[0] are basically a search engine that understands the text it's searching through... and then can do a hundred different things with it. I found myself writing better notes at work for this very reason - because I know the LLM can see them, and can do anything from surfacing obscure insights from the past, to writing code to solve an issue I documented earlier.
It makes notes no longer be write-only.
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[0] - Which, incidentally, is itself enabled by LLM embeddings.