Comment by nextaccountic

> I wish people would understand what a large language model is. There is no thinking. No comprehension. No decisions.

> Instead, think of your queries as super human friendly SQL.

Ehh this might be true in some abstract mathy sense (like I don't know, you are searching in latent space or something), but it's not the best analogy in practice. LLMs process language and simulate logical reasoning (albeit imperfectly). LLMs are like language calculators, like a TI-86 but for English/Python/etc, and sufficiently powerful language skills will also give some reasoning skills for free. (It can also recall data from the training set so this is where the SQL analogy shines I guess)

You could say that SQL also simulates reasoning (it is equivalent to Datalog after all) but LLMs can reason about stuff more powerful than first order logic. (LLMs are also fatally flawed in the sense it can't guarantee correct results, unlike SQL or Datalog or Prolog, but just like us humans)

Also, LLMs can certainly make decisions, such as the decision to search the web. But this isn't very interesting - a thermostat makes the decision of whether turn air refrigeration on or off, for example, and an operating system makes the decision of which program to schedule next on the CPU.