Is it bad I think this is a decent high level algorithm? As in, the user then filling in details or asking the assisting model to fill them in via subsequent prompts seems like a solid start to a problem solving session.
If you are asking about aerospace engineering concepts at the level of a PhD thesis... it just hand-waves away problems in ways that, when pressed, it cannot give detail on.
So, as far an a algorithm goes, the problem becomes determining the computational complexity of the 'hand waving' part. For writing reactJS frontend APIs it seems like it's pretty damn low and thus the AI can spit out code and even fix bugs for you. For developing something that actually took a human 7+ years of undergrad, graduate, and PhD work to ascertain and work out.... no chance.
But yes that general algorithm works for all walks of life essentially and I keep getting a format like that for everything setup I ask about, as if it was trained on 'How to answer a hard technical question in these easy steps...' and it is sticking to that.
Would it be safe to assume the limiting factor with this or any algo of this type might always be availability of context/data, and these niche questions might always be an edge case?
Are there alglrithms that can reliably extrapolate "new" data/context that does not exist yet and is accurate IRL?
Sorry if my terminology or understanding is way off, I don't know much about this field.
There is something a bit more needed - Meta did a deep model recently with training specifically more on scientific papers, and it is able to 'talk the talk' better but unfortunately the 'understanding' just isn't there.
I think at the end of the day, programming languages are languages after all, thus work well with these tools, but there is something harder about scientific reasoning and interdisciplinary planning that is going to take something a bit more. This is the main goal of various research into bring symbolic methods back into interface with deep learning models.