LLMOps: Definition and Examples

LLMOps is a discipline derived from MLOps, specifically adapted to the unique challenges posed by large language models (LLMs). While traditional MLOps focuses on training and deploying classical machine learning models, LLMOps incorporates issues specific to LLMs: prompt management, API call orchestration, inference cost control, text output quality evaluation, and implementation of guardrails.

The LLMOps pipeline covers several key stages. First, model selection and configuration (choosing between a proprietary model via API or an open source self-hosted model). Then, prompt engineering and possible fine-tuning to adapt the model to a specific use case. Next, production deployment with management of scaling, caching, and latency. Finally, continuous monitoring of performance, costs, and response quality.

An essential component of LLMOps is managing prompts as versioned artifacts. Unlike classical ML where code and data are sufficient to reproduce a result, LLMs require rigorous tracking of prompt templates, inference parameters (temperature, top-p), and reasoning chains. Tools like LangSmith, Weights & Biases, or Humanloop allow versioning, testing, and comparing performance of different configurations.

LLMOps also includes setting up automated evaluation systems (evals), managing RAG (Retrieval-Augmented Generation) to connect models to knowledge bases, and implementing security policies to filter inappropriate content or hallucinations. It is a rapidly maturing discipline that becomes essential for any organization deploying large-scale LLM-based applications.