Your LLM's benchmark score is lying about production

Leaderboard scores measure isolated model accuracy, not production readiness. The real tradeoff triangle—accuracy, performance, cost—means you can only optimize two. Teams often neglect system evaluation until users complain.

System evaluation metrics (time to first token, throughput, SLOs) reveal bottlenecks. Workload shape matters: chat, RAG, and agents have wildly different token distributions. The inflection point where latency spikes defines true capacity.

Agents compound complexity: each step—intent understanding, tool selection, retrieval—is a failure point needing its own evaluation. The common mistake is starting with domain accuracy before ensuring system stability.

The bottom line: test with your data, realistic traffic patterns, and your own success metrics. Ignore leaderboard hype and benchmark scores as the finish line.