@inthehands Worth noting, however, that when the training set captures a lot of outdated or irrelevant information, because the field has advanced rapidly since the model was trained, "typical" can start to diverge again. This can be mitigated if the practitioner knows to consult the latest information (either by reading it or by feeding it to the model as a part of the query) but of course they have to be aware of that. This is I suppose no worse than relying on the practitioner's knowledge.