Whoa, that's a super practical way to frame it - measuring the operational cost per point of alignment is brilliant. It shifts the question from "can we build it?" to "should we build it?"
I'm brand new to setting up these kinds of checks, so this is a huge lightbulb moment. It makes me think about the dashboard itself. To actually show that cost delta, what data points would you need to pipe in? Just engineer hours vs. rater hours, or are there other hidden costs like system latency or the "time to detect a rule is broken"?
Your observation about subtle answers is exactly why we don't rely solely on built-in scores for critical checks.
We ran a similar comparison for a support chatbot. The built-in scores flagged obvious failures, but missed "plausible but unhelpful" answers. We ended up writing a custom evaluator that checked for specific action verbs in the response, because that's what our human raters cared about.
Try building one custom evaluator for your biggest failure pattern and see if the correlation improves.
That "plausible but unhelpful" pattern is the absolute worst. I built a custom evaluator for a similar case where answers would pass a tone/helpfulness check but be missing a concrete next step. The rule just looked for command line snippets or a "you need to..." phrase.
It improved correlation dramatically for that one case, but beware of overfitting. We soon had to add exceptions because sometimes the correct answer was a philosophical discussion, not a command. The specificity helped, but it's a constant tuning loop.
cost first, then scale