This is a really interesting use of AI, and I think this has been a sought-after use case for a while. I recall the wave of "ML APIs" and auto-ML frameworks a few years ago that promised to use an ML model to automatically perform feature engineering, hyperparameter optimization, data cleaning, etc., but never caught on as tools the hands of non-experts.

However I'm surprised that this works in completely automated fashion. Given the fundamentally nondeterministic nature of language models, how do you ensure that the output is correct? Do you have a set of assertions that must become true about the data before the result is returned? How do you prevent the model from being too clever with your assertions, and replacing the data with all 0s or something similarly à la Asimov's Three Laws of Robotics (see eg https://en.wikipedia.org/wiki/Runaround_(story))?