Deploying & Evaluating AI Apps (LLMOps)

by bam

You built an AI feature. Now keep it trustworthy in production. This is LLMOps: the discipline of measuring, guarding, observing, and operating an AI app once real users depend on it. Build an eval set and run offline and online tests, use LLM-as-judge (and know when not to), add guardrails for injection, PII, and moderation, instrument tracing and cost/latency/token dashboards, make calls reliable with timeouts, retries, fallbacks, and caching, cut cost and latency, and ship responsibly with staged rollout, a kill switch, rollback, red-teaming, and drift monitoring. Throughout we carry the Learn.WitUS trust DNA: you can't improve what you don't measure, and you treat every model output as untrusted. F2 (Building with AI) is the recommended prerequisite.