How grounding and citations make AI-generated answers verifiable and trustworthy

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Large language models generate fluent, confident text but cannot reliably distinguish fact from fabrication, making verification a serious challenge. Grounding addresses this by forcing a model to generate answers strictly from a set of retrieved source passages, preventing it from drawing on outside or invented knowledge. Citations attach source markers to individual sentences, allowing each claim to be audited independently rather than accepted as a whole. A simple lexical overlap check — comparing content words in a claim against its cited source — can automatically flag unsupported statements before they are published. Stronger verification uses natural language inference models to test whether a source actually entails a claim, handling paraphrase and negation more robustly than word-matching alone.

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