Enterprise LLMOps
The operational discipline that turns an LLM demo into a governed production system — versioning prompts, evaluating quality and safety, observing behaviour and token cost, and closing the feedback loop. It extends MLOps to handle non-determinism, retrieval, and inference cost.
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LLMOps is the operational discipline that turns an impressive LLM demo into a dependable production system. A demo needs a good prompt and a capable model; a production system needs versioned prompts, evaluation you can trust, observability into behaviour and token cost, engineered guardrails, and a governance loop that catches regressions before customers do.
It extends MLOps to handle what LLMs add: non-deterministic output, retrieval pipelines (RAG), safety, and inference cost that scales with every request. The order that works is deliberate — evaluate first, then version, observe, guardrail, gate every change, and close the feedback loop — because optimising what you cannot measure only produces confident regressions.
Explore the cluster below: how to build these systems (Enterprise AI Development), ground them (RAG Systems), and run agentic ones safely (AI Agents).