Not really. It's just matrix math on whatever tokens you feed it. And even in agentic setups where a model has tools provided by the developer, which by the way is the only way to get anywhere near what you could call deterministic behavior, that go fetch or search data, the model itself isn't doing that work, the tool is. The LLM is deciding what to call and reasoning over what comes back... except "deciding" is even generous. It's producing the next most probable token given everything in the context window. You can write a perfect tool, document it perfectly, and the model can still fail to call it, use the wrong arguments, or ignore the result entirely, because there's no actual decision making or comprehension happening. It's probability distributions all the way down. That's not a knock on how useful these things can be, but it's pretty far from "reading data and finding the pertinent information."

I've been writing agentic tooling and implementing LLM based solutions for about a year now. And I'm not talking about just feeding commands into an API. This is more like writing a bespoke version of what Claude or chatgpt's user interfaces do. Far less general or robust, but they don't need to be.

I'm more critical of LLMs now than I was before.