Why AI Citations Are Often Wrong and How Grounded Systems Fix That

Most AI systems that are prompted to cite sources produce references that point to irrelevant or incorrect text, a problem rooted in how language models generate tokens without maintaining links to source material. Grounding requires that every factual claim be logically supported by a specific retrieved passage, while attribution maps each generated sentence to its source — and a reliable system needs both. Tools like Google's Check Grounding API address this by scoring each claim against evidence passages using an entailment model, flagging only those that meet a confidence threshold. A more effective architectural fix, illustrated by Perplexity's approach, assigns stable citation identifiers to passages before generation so the model copies existing markers rather than inventing them. Building truly verifiable AI answers requires combining strong retrieval, structured context, external verification, and training that rewards the model for refusing to answer when evidence is insufficient.