Natural language specifications often aren't specifications at all: interpreting them requires context that is not available to the computer, and often not even available to the specification's authors without further research / decision-making.

LLMs address this problem by just making things up (and they don't do a great job of comprehending the natural language, either), which I think qualifies as "hoping for the best", but I'm not sure there is another way, unless you reframe the problem to allow the algorithm to request the information it's missing.