The question is how well it does so. IME it depends; sometimes it does a fairly good job, sometimes it 'overlooks' crucial points. It also depends a heck of a lot on how much prior data is available on the question asked, and the fuzziness with which seemingly related, but different instances are going to be included. E.g. when asking about the "Sigma 105mm f/2.8 DG DN Macro Art and the Panasonic Lumix S 100mm f/2.8 Macro", there's a good chance that data associated with other Sigma Art lenses, other brands of 105mm lenses, Panasonic Lumix cameras (instead of lenses) etc. etc. will be included in the linguistic mix. Since AI cannot differentiate between such things, as long as the letters are sufficiently similar, it'll all treat it as part of the same complex. In comparisons like this one, this can become a major problem.
If AI is to be used in a case like this, I'd always try to use a tool that at least allows for checking its references. Something like Perplexity for instance:
https://www.perplexity.ai/search/out-of-these-two-which-is-the-n1htxzc.TRm7YgWSg_tZDQ While that does allow for tracking the sources used, note that the answer (as is typical for LLM's) is for the most part tangentially related to the actual question at best, and consists to a large extent of irrelevant filler.