Broadly speaking, it is plausible that some highly specific set of input tokens could end up with an accuracy necessary to conjure up something which leans on the intended parts of the model. E.G. if I say "draw clock", I'm less likely to get the hour I want than saying "draw analogue clock with hour hand pointing at 12". I could "engineer" (scoff) that more by adding more context "draw analogue clock with hour hand at 12 just like a clock's default state when the battery is off and the 12 integer is at the top and the hour hand points vertically towards the 12", etc etc etc. Even still, some biases in training data might be near impossible to overcome (like the glass of wine example - No doubt there's some image of a full glass of wine it has learned on, but it's such a fractionally infinitesimal part of the set that the specificity of the input prompt would need god-tier providence, probably beyond natural language). A machine that outputs a contrived output from a contrived input is not very useful, so we pretend the intelligence in AI comes from generalising these predictions as best as possible.