Video by H2O.ai via YouTube
AI can be confidently wrong. Here is why it happens, how grounding fixes it, and how to measure whether it worked.
Terms covered:
– Hallucination: models are trained to generate statistically likely text, not to verify facts. When a model does not know, it fills the gap with something plausible.
– Grounded Generation: anchoring responses in verified, retrieved sources so answers are traceable and far less likely to be fabricated
– LLM Evaluation: assessing outputs for accuracy, relevance, coherence, cost and safety, as an ongoing practice rather than a one-time check
– RAG Evaluation: measuring retrieval and generation separately, because a system can retrieve well and generate poorly, or generate fluently from the wrong sources
The goal is not perfect AI. It is trustworthy AI, and that requires knowing how to measure it.
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AI Fundamentals Glossary | H2O.ai University
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