1. The way I think about these huge models is no longer as something that makes predictions, but rather as a kind of huge knowledge base (compressed training data as representations) with smart query capabilities, which come from the fact that the model is constrained to create syntactically correct output. This could be the next generation of search. There is a fine line between search and creating novel outputs, since most novelty is just a combination of old things with constraints on correct syntax.
2. The shown results are likely a result of massive cherry-picking in the typical "pitch deck demo" fashion. You'll see a lot more failures when trying to use this and you need to get the query "just right" for it to produce good output, which a skill in itself. Also let's not forget that the model must be fed fine-tuning examples, which also need to be "just right.". That's why I believe these kinds of models aren't that useful for fully automating things - for something to be used in production it should be 100% correct - but they are very useful as a search function and alternative to e.g. StackOverflow in this case. Querying the model will give you good enough results that you can use as a starting point for your own use case.