How we score speaking when "native-like" is the wrong target - the eval rubric behind Elispeak I build Elispeak, an AI English speaking coach. The first article in this thread covered what was technically hard. The second covered the user-profile layer that makes Eli (the tutor persona) feel like it remembers you. This one is about the piece that sits underneath both: the eval rubric that decides what "you got better today" actually means. It is the smallest, driest part of the product. It is also the part that keeps every other part honest. If the rubric is wrong, every weakness flagged in the user profile is wrong, every recommendation is wrong, and every "you levelled up" message is a lie. The wrong target The default speaking-coach pitch is "talk like a native." That target is broken in three specific ways. It is not what the user is hiring you for. A QA engineer in Lviv preparing for a hiring panel does not want to sound like a Texan.…