Your question is LLM Evaluation With Scarce Ground Truth. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What strategies would you employ to evaluate an LLM's performance in a production environment where ground truth is scarce at Nscale?
Discuss how you would combine offline benchmarks, human review, proxy metrics, user feedback, automated evaluators, and production monitoring. Explain how you would detect regressions, account for non-deterministic outputs, validate evaluator reliability, and balance quality, latency, cost, and safety. Identify limitations and describe how you would turn evaluation results into model or prompt improvements.