◇ Could this help me?
Yes. The technical claim at the center of this is accurate and worth internalizing: reinforcement learning post-training depends on a scoreable signal, which is why capability has moved fastest in math and code and slowest in domains where quality is a matter of preference. Framing taste as contextualized expert preference is a useful working definition because it makes the generalization question concrete rather than mystical, and it explains why the industry treats taste as a research problem instead of an aesthetic complaint. General practice, as background that sharpens judgment about where model output can be trusted and where a human still has to be the evaluator.
I want to work out where AI output in my domain can be trusted and where it cannot. Explain the difference between verifiable domains, where a reward function can deterministically score an output, and taste-driven domains, where quality depends on subjective human preference. Then apply that split to the specific work I do: list which of my tasks have a checkable ground truth and which do not, and for the ones that do not, propose what a good human evaluation rubric would need to capture. Be concrete about which parts I should still be the judge of.