Running enterprise GenAI reliably requires treating it like any production service with explicit SLOs, cost budgets, and ownership. Key disciplines include defining a production contract with p95 latency and cost-per-request targets, treating retrieval-augmented generation as the core system with access controls and quality metrics, building an evaluation harness early that runs in CI, instrumenting the full pipeline with per-request traces, using model routing and caching to control token costs, and designing graceful degradation modes. A minimum viable checklist covers SLOs, retrieval ownership, evaluation suites, tracing, routing, degradation testing, and incident runbooks.
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