LLMOps
Operational practices required to manage the lifecycle and monitoring of large language models.
Definition
LLMOps (Large Language Model Operations) is the set of practices, tools, and workflows required to develop, deploy, monitor, and maintain large language model applications in production. It extends the principles of MLOps to address the unique challenges of LLM-based systems, including prompt management, context handling, cost control, and evaluation at scale.
Key characteristics of LLMOps include:
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Prompt Lifecycle Management: LLMOps treats prompts as versioned artifacts, tracking changes to system prompts, templates, and few-shot examples with the same rigor as code versioning, since small prompt changes can dramatically alter model behavior.
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Observability and Tracing: Production LLM systems require tracing through complex chains of retrieval, tool calls, and generation steps. Tools like LangSmith, Langfuse, and Arize provide end-to-end visibility into each request's execution path.
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Cost Management: With LLM API costs scaling per token, LLMOps includes monitoring token usage, optimizing prompt length, implementing caching strategies, and selecting appropriately sized models for each task.
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Evaluation Pipelines: Automated evaluation runs test suites against model outputs after each change, catching regressions in quality, safety, or factual accuracy before they reach production users.
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Feedback Loops: LLMOps platforms capture user feedback, flag low-quality responses, and feed this data back into prompt refinement and fine-tuning cycles, creating continuous improvement workflows.
For teams running agentic systems, an Agentops Dashboard consolidates observability, cost tracking, and evaluation metrics into a single operational view.