The LLMOps loop

Traditional software ships on a line: build → test → deploy → done. AI features run on a loop, because the four gap-sources from the last lesson never stop operating. LLMOps is that loop, and the whole course is one turn around it.

        ┌──────────────────────────────────────────────┐
        ▼                                                │
   EVALUATE ──▶ DEPLOY ──▶ OBSERVE ──▶ IMPROVE ──────────┘
   (does it     (ship a    (what's     (fix, informed by
    work? on    version,    actually    real failures →
    what set?)   gated)      happening?) new eval cases)

   Every real failure in OBSERVE becomes a case in EVALUATE.
   The loop tightens over time — or the feature rots.

The four stations

• Evaluate — measure quality against a golden set before shipping. You know this from three courses; LLMOps makes it continuous and automated (Module 2).
• Deploy — ship a specific, versioned configuration behind a gate, with the ability to roll back. Not 'update the prompt in production' — a controlled release (Module 4).
• Observe — capture what actually happens on real traffic: traces, metrics, failures. Not 'is it up' but 'is it right, fast, and affordable' (Module 3).
• Improve — feed real failures back into the eval set, fix, re-evaluate, redeploy. The failures you find in production are the test cases you were missing.

Why it must be a loop

A launch is a snapshot; the reliability gap is a moving target. Model updates, corpus growth, and shifting user behavior guarantee that a system correct at launch drifts out of correctness — silently. The loop is what converts that inevitability from 'we found out when a customer complained' into 'our Tuesday eval run caught it and the gate held.' Teams with the loop improve every week and know they're improving; teams without it ship once and slowly degrade in the dark.