Earlier quoted context omitted.
I use llms exactly and exclusively for the first two cases - just write comments like: // map this object array to extract data, and use reduce to update the hasher And let llms do the rest. I rarely find my self back to the browser - 80% of the time they spit out a completely acceptable solution, and for the rest 20% at least the function/method is correct. Saved me much time from context switching.
For me the quick refresh is better as I only need to do it once (until I don't use the language/library again) and that can be done without internet (local documentation) or high power consumption (if you were using local models). And with a good editor (or IDEs) all of these can be automated (snippets, bindings to the doc browser,...) and for me, it's a better flow state than waiting for a LLM to produce output. P.S…
That's why I almost never use the 'chat' panel in those AI-powered extensions, for I have to wait for the output and that will slow me down/kick me out of the flow.
However, I still strongly recommend that you have a try at *LLM auto completion* from Copilot(GitHub) or Copilot++(Cursor). From my experience it works just like context aware, intelligent snippets and heck, it's super fast - the response time is 0.5 ~ 1s on average behind a corporate proxy, sometimes even fast enough to predict what I'm currently typing.
I personally think that's where the AI coding hype is going to bear fruit - faster, smarter, context+documentation aware small snippets completion to eliminate the need for doc lookups. Multi file editing or full autonomous agent coding is too hyped.