> people vastly underestimate full text search

It is not psychological, it is fully justified: substring search cannot find synonyms, periphrases and mistaken neighbours.

> It is not psychological, it is fully justified: substring search cannot find synonyms, periphrases and mistaken neighbours.

It is, if people don't even stop to think if they need synonyms, periphrases, or mistaken neighbours.

As the blog post points out, more often than not you don't, particularly if your primary usecase is to search for technical keywords or codenames.

	▲	lopsotronic 2 minutes ago \| parent [-]
		Precisely this. The people in charge of technical direction don't understand the fundamentals of the technology. So you get the idea that LLMs can help make sense of parts data. Which . . . no, no it really can't, not without ALSO plugging in basically every other hunk of natural language you might have laying around. Unless you think PLG HT HFI is just a natural synonym of HOT PLUG INJECTOR, in which case you're just quantitatively wrong. Vectors and LLMs are great, but there's no magic pill here. If your parts data and config management[1] is all crazy, that's an institutional problem. Buying a crapton of tokens isn't fixing it, unless you're using it to help build an actual formal solution based on good fundamentals. [1] Such as it is.