It is more subtle than that. It is trained on "everything" so the specific adjectives and words you use in your prompt will cause it to generate very different

It is more subtle than that. It is trained on "everything" so the specific adjectives and words you use in your prompt will cause it to generate very different responses. The fine tuning causes it to prefer by default more reliable/professional responses ... but that is not because the training data is weighted toward them as such. If you mention specific publications, or forums, it will give you responses more likely to come from those. Looking at the reasoning traces for for the new reasoning models you can actually see how fine tuning is moving toward having models list the assumptions around data sources, which should be trusted, list multiple perspectives and then summarize, resulting in better answers. You can do that today with non-reasoning models, but you need to prompt engineer it to ask for that explicitly. This process of identifying not just extant content, but teaching systems how to approach problem analysis (instruction tuning, reasoning traces, etc ...) will be key to influencing how the models work and increasingly how they are differentiated.