LLMOps is the practice of running applications built on large language models in production, from prompt versioning to evaluation and monitoring. It extends MLOps for systems whose behaviour depends on prompts, retrieved context and external models.
An evaluation set is a fixed collection of inputs with expected outcomes, used to score an AI system the same way every time it changes. It plays the role a regression test suite plays for ordinary code.
Guardrails are checks placed around a language model that inspect what goes in and what comes out, blocking or correcting responses that break rules. They enforce policy in code rather than trusting the prompt.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
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