There is a field of AutoML, with its own specialized academic literature and libraries that tried to achieve this type of thing but didn't work very well in practice.

Years ago there were big hopes about bayesian hyperparameter optimization, predicting performance with Gaussian processes etc, hyperopt library, but it was often starting wasteful experiments because it really didn't have any idea what the parameters did. People mostly just do grid search and random search with a configuration that you set up by intuition and experience. Meanwhile LLMs can see what each hyperparameter does, it can see what techniques and settings have worked in the literature, it can do something approximating common sense regarding what has a big enough effect. It's surprisingly difficult to precisely define when a training curve has really flattened for example.

So in theory there are many non-LLM approaches but they are not great. Maybe this is also not so great yet. But maybe it will be.