LLMs are part of soft-computing, i.e. contrary to traditional (algortihm-based) computing sometimes things won't get the right result (or, just as bad, the righ

LLMs are part of soft-computing, i.e. contrary to traditional (algortihm-based) computing sometimes things won't get the right result (or, just as bad, the right result in the wrong format). Engineering solutions with LLM is a lot of fiddling, which is experimental rather than analytical/logical. It is worth getting use to that mind-set, and then use LLMs as a tool (they are likely here to stay, because big tech have started to integrate features based on them everywhere, for better or worse). So this is your option (2.). Personally, I prefer software I use NOT to be smart, but to be 100% deterministic. But already my favorite LaTeX authoring environment (Overleaf) has a button pop up called "fix this" that auto-resolves syntax errors, many of which overwhelm my doctoral students that no longer read books end-to-end (here, to learn LaTeX). Gradually, you may dive deeper into the "how", driven by either need or curiosity, so eventually you will probably have done both (2.) and (1.). - in the same way that you will have first learned SQL before learning how replication, transactions, data buffers and caches, or query optimizers are implemented.