Models crush benchmarks but fail in real apps. What’s the checklist for reliability, cost, and monitoring?
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Production trifecta: Offline evals (Ragas 85%+), Online A/B (10% traffic, retention metric), Shadow mode (silent eval on prod data). Guardrails: Lakera for jailbreaks. SLOs: 99.9% uptime, <3% error rate. Dashboard: Weights & Biases + custom Slack alerts. Model registry via MLflow. Client went live with 99% confidence.
Hey, 5 key gates:
1) Latency P95 <2s
2) Hallucination rate <2% on adversarial prompts
3) Cost < $0.01/query
4) Drift alert when accuracy drops 5%
5) Human eval 95% preference vs baseline.
Tools: LangSmith + Phoenix tracing.
Fail any gate = iterate. Deployed CS bot passed all, saved 70% headcount.