useful. One is grounding. And so grounding roughly means that you have some source material and in machine learning generally you want to have some notion of supervision. There's something you're learning from. And in messy situations we don't necessarily always have clean supervision, but we can get pretty reliable supervision if we kind of are careful about techniques we use. And so grounding is one where you have some source material and the ability to do an AB test of like with and without is a very useful way of creating this kind of capability gap where a model will do better if it has something in context. And this gap is something we can exploit to create signal that we can then learn from. Judges are also really useful.