How Do You Benchmark LLMs for Your Use Case? (EXPLAINED)
Hard project question on use-case benchmarking — golden sets, task metrics, regression gates, and leaderboard limitations.
TL;DR — Quick Answer
Public leaderboards (MMLU, etc.) measure broad capability — not your RAG faithfulness, tool accuracy, or latency SLA. Build a **domain golden set** from real queries (PII-scrubbed) with human labels; define task metrics (accuracy, faithfulness, citation match, pass@k for code); run pairwise eval across candidate models; measure p95 latency and cost per success; gate releases on regression thresholds. Include adversarial and abstention cases. Re-benchmark when prompts, retrieval, or tools change — not just model swaps.
The Interview Question
How do you benchmark LLMs for a specific production use case rather than relying on public leaderboards?
Deep Explanation
Why leaderboards fail
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