Comment by vintermann

The appeal is that you can incorporate more context in a decision problem without knowing in advance what that context might look like.

Take the problem of determining if two people in a genealogy database represent the same person. It's a decision problem. There will be some data in structured formats you can count on, but which may be missing, or may even be wrong. There will be lots of fuzzy data (born in Newtown yes, but which Newtown?) and unstructured data (notes, etc.)

Now, do you want to try to assemble a dataset to train your own classifier on that, or do you want to throw a general purpose LLM-rooted decision model on it?