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.
A hallucination is when a language model states something false or invented with the same confidence as a fact. It comes from how models generate text, so it can be reduced and managed but not patched out like a bug.
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.
Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
An AI product manager decides what AI-powered features to build and how to judge them, balancing user needs with model capabilities, data, evaluation and risk. They define success when outputs are probabilistic rather than fixed.
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.
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