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.
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.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
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.
Explainable AI is the set of methods that make a model's outputs understandable to people. It shows which inputs drove a prediction and how far that prediction can be trusted.
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