I will say the ones where Claude did better was technical in nature. But.. still experimenting.
I will say the ones where Claude did better was technical in nature. But.. still experimenting.
I don't know what the deal is, but it's a failure state I've seen consistently enough that I suspect it has to be some kind of issue at the intersection of training material and the long context window.
I am sure there is a technical skill in getting Claude to shut the hell up and answer, but I shouldn't have to suss out its arcane secrets. There should be a checkbox.
Also it's useful to ask questions that you already know the answer to, in order to understand its limits and how it fails. In that case, "better" means more accurate and appropriate.
extending that to an LLM, perhaps language translation sits as a "3rd type" on top of those three types.. translating a question or answer into another spoken language.. or via an intermediate model of some kind .. but that is going "meta" ..
the point is, there are different kinds of questions and answers, and they dont all fit in the same buckets if "testing" an LLM for better..
One time I asked about reading/filtering JSON in Azure SQL. Claude suggested a feature I didn't know of OPENJSON. ChatGPT did not, but used a more generalize SQL technique - the CTE.
Another time I asked about terror attacks in France. Here Claude immediately summarized the motives behind, whereas ChatGPT didn't.
Lastly I asked for a summary of the Dune book, as I read it a few years ago and wanted to read Dune Dark Messiah (after watching part 2 of the 2024 movie, which concludes the Dune 1 book). Here ChatGPT was more structured (which I liked) and detailed, whereas Claude's summary was more fluent but left out important details (I specifically said spoilers was ok).
Claude don't have access to searching internet or making plots. ChatGPT seems more mature with access to Wolfram alpha, latex for rendering math, matplotlib for making plots etc.