Applied LLMOps

A search assistant that answers beautifully in a demo can still be unshippable — too slow for the slowest five percent of its users, too expensive per query, or simply down the moment its LLM is. Keeping a patient-facing search and assistant alive in production is therefore a numbers problem before it is an architecture problem, and that is how this module treats it. You will state the non-functional frame first (queries per second, latency targets, availability, and what a degraded mode looks like), then allocate the end-to-end latency budget across the answer path, design the fallbacks for when that budget is blown, and add the ML-specific observability that ordinary service metrics miss. The same territory reappears from the cross-model fleet angle in P13's Production ML and MLOps pillar.