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Summary: LLMOps

LLMOps is the operational layer that keeps an LLM application working over time: the LLM analogue of DevOps and MLOps. Five engineering pillars: observability (log enough per request to debug, the 7-10-field discipline that grows from lesson 5); evaluation in production (sample and score live responses, A/B test prompt/pipeline changes, route poor responses for human labeling, feed back into the offline test set); prompt versioning (treat prompts as code, source control + version + registry, version recorded in the logs); cost and latency monitoring (per-request and aggregate dashboards, budget and p50/p95 regression alerts; cost-per-resolution when it is the right business metric); regression testing (grow the lesson-3 test set to 100s-1000s; run before every prompt or model change; refuse merges that regress; new failures become new test examples; makes model upgrades safe). The smallest practical first stack takes days, not months. The tools matter less than the discipline. Taught as engineering throughout; incident-disclosure policy, vendor-failure/SLA/liability, and compliance-framework questions are real but out of scope here.

Core ideas

Section titled “Core ideas”
  • LLMOps = engineering discipline for running LLM apps over time. The LLM analogue of DevOps and MLOps. Five pillars.
  • Observability. Log per request: prompt + version + model + params + retrieved context ids + tool calls + response + token counts + latency + cost + user feedback + trace id. Without these fields, debugging is guessing.
  • Evaluation in production. Sample live responses; score with rubric (LLM-as-judge or human); surface poor responses for human labeling; A/B test prompt/pipeline changes on real traffic. Prove changes helped; do not assert.
  • Prompt versioning. Source control + a version constant + an optional registry; the deployed version is in the logs so date-of-change correlates with quality changes; staged rollouts with rollback ready.
  • Cost and latency monitoring. Per-request + aggregate dashboards by route / model / prompt-version / user-segment; budget alerts; p50/p95 latency regression alerts; sometimes cost-per-resolution as the business metric.
  • Regression testing. Grow the lesson-3 test set; run before every change; refuse merges that regress without explicit override; new production failures become new examples. This makes model upgrades safe.
  • Tools < discipline. LangSmith / Langfuse / Helicone / Arize / Weights & Biases Prompts all implement the same five pillars. Picking one is budget/stack-fit; the practice is what wins.
  • Out of scope: incident-disclosure policy, vendor-failure / SLA / liability, compliance frameworks (SOC 2, ISO, sector-specific). Real but require their own framing.

What changes for you

Section titled “What changes for you”

LLMOps is the unglamorous discipline that decides whether an LLM application stays good after launch. Without it, prompt changes ship without checks and regress silently; cost grows unmanaged; model upgrades quietly break the output format; users report vague “it feels worse” experiences that take weeks to triage. With it, every change is measurable, every regression catchable, every cost spike investigatable, and the team moves faster because they deploy with confidence. The discipline is small to start (lesson 5’s logging + lesson 3’s prompt-versioning + a 20-50-example test set = a working LLMOps practice) and grows naturally as the application matures. Phase 2 closes here; the application is now buildable, shippable, usable, and operable. Phase 3 turns to the field’s direction, when and how to consider training your own model, agents, and the broader landscape.

LLMOps is the discipline that keeps an LLM application working over time. Observability, evaluation in production, versioned prompts, cost and latency monitoring, and regression testing turn “ship and hope” into “ship and know,” and they are most of what separates a demo from a production system.