LLM output quality monitoring
Design the production quality-monitoring system for a customer-support LLM assistant handling millions of conversations a day. Unlike a classifier there's no single accuracy number, and outputs are free-form text that can be subtly wrong, unhelpful, off-policy, or hallucinated. You need to detect quality regressions (e.g. after a prompt change or model upgrade) quickly, flag harmful/hallucinated responses, and do it without humans reading every conversation. Design the monitoring + evaluation system for generative output at scale.
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:00 left
Estimate5:00 planned
Design15:00 planned
Deep dive12:00 planned
Failure8:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.