What signals matter most when optimizing cost without hiding quality loss?

Example Answer

The way I'd think about it is this: I look at cost together with task success, severe error rate, fallback rate, escalation quality, and segment-level regressions. If I only watch spend and latency, I can easily ship an optimization that looks efficient and quietly weakens the product.

I also pay attention to distribution shifts. Sometimes average quality stays flat while one important workflow degrades or one customer segment gets pushed onto the cheap path too often.

A good cost optimization is one where the savings are real and the quality tradeoff is either negligible or explicitly chosen. If the quality loss is hidden in the measurement, the optimization is not trustworthy.

What matters in an interview is not only knowing the definition, but being able to connect it back to how it changes modeling, evaluation, or deployment decisions in practice.

Common Poor Answer

A weak answer is tracking tokens per request and average quality score only. Cost optimization needs workflow and segment visibility too.