Interesting project, but I think it's solving the wrong bottleneck. The gap between what I want and what the model produces isn't primarily a language problem — it's a knowledge problem. You can write the most precise spec imaginable, but if the model doesn't have domain-specific knowledge about your product's edge cases, undocumented behaviors, or the tribal knowledge your team has accumulated, the output will be confidently wrong regardless of how formally you specified it.

I've been working on this from the other direction — instead of formalizing how you talk to the model, structure the knowledge the model has access to. When you actually measure what proportion of your domain knowledge frontier models can produce on their own (we call this the "esoteric knowledge ratio"), it's often only 40-55% for well-documented open source projects. For proprietary products it's even lower. No amount of spec formalism fixes that gap — you need to get the missing knowledge into context.