You can definitely use it as an incredibly powerful learning tool. I've been using it to guide me through Haskell and I've learned so much more than I did in college when I had spent way more time on it (that time being 10 years ago so I had forgotten most of it). It's invaluable being able to go and ask the AI questions and have it rephrase the parts you don't understand. I spent a good hour with it figuring out exactly what the traverse function does (`traverse (\x -> ['z', x]) "ab"` probably doesn't do what you think it should).

Spoiler: The <> combines lists as cross joins (for-loop within another for-loop). The lambda maps to new lists ['z', 'a'] and ['z', 'b'] (strings are just char arrays). It then computes the various combinations to recombine them so ['z', 'z'], ['z', 'b'], ['a', 'z'], ['a', 'b']. Note it's not all permutations since you don't have ['z', 'a'] even though that's actually one of the "base" arrays we got from the lambda. It took me a long time to really internalize that and it's still probably not a function I'd reach for.

It is kind of cool, though, that you can do `(,) <$> lista <> listb` and you get a nested for loop.

I've been getting the most value out of LLMs this way as well. I don't care to vomit out code for the fastest MVP. I want to learn and understand what goes into what I'm building, piece by piece. So I use LLMs as a research and learning assistant to speed up gaining those tidbits. But I still have to understand the code well enough to put the pieces together by hand.