Current LLMs can’t reason like that. If their training text contained a question and response similar to the one they are asked, those will have been encoded in

Current LLMs can’t reason like that. If their training text contained a question and response similar to the one they are asked, those will have been encoded in their network weightings, and they will produce an answer of a similar form but randomly varying in the details. This is why they produced answers to maths questions in the right form, but wrong in detail. It’s why when asked when and where a famous person was born, they will often respond with an answer that looks highly plausible, but wrong in the details. What this mechanism might do though is enable it to identify this as a question best answered by one of it’s available APIs. That API might be to an LLM trained to write code to solve maths problems, so that will write the code, run it and generate a response, then pass that back through the API. So this way you could build a hierarchy of problem solvers, some of them procedural, some of them domain specific LLMs, or problem solvers that themselves use LLMs as components to help solve that problem domain. Interesting times.