LLMOps
LLMOps is the subset of MLOps focused on the specific operational concerns of large language models: prompt versioning, evaluation, cost control, and output observability.
What is LLMOps?
LLMOps adds practices that classical MLOps does not cover well. Prompts are first-class artefacts. Evaluation uses LLM-as-judge alongside golden datasets. Cost is metered by token, not by request. Latency is dominated by streaming and context size. Outputs are non-deterministic and need sampling and content checks. Tools include prompt registries, eval harnesses, trace viewers, and guardrail engines.
How does LLMOps apply to enterprise AI?
Any enterprise with a generative AI feature in production needs LLMOps. Without it, the team cannot debug why a prompt regressed, why costs spiked, or why a customer received a wrong answer.
Related terms
- MLOps - MLOps is the discipline of operating machine learning systems in production: versioning, deployment, monitoring, retraining, and governance.
- Evaluation Harness - An evaluation harness is the test framework used to measure an AI system against a fixed set of inputs, expected outputs, and metrics, run on every change.
- Observability - Observability for AI is the ability to understand what an AI system did, why it did it, and at what cost, by inspecting its inputs, outputs, intermediate steps, and metrics.
- Guardrails - Guardrails are runtime checks placed around an AI system to constrain inputs, outputs, and tool calls within safety, compliance, and business policy.
External references
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