Hallucination
When a model produces plausible-sounding but false or fabricated output.
Hallucination occurs when a large language model generates output that sounds confident and coherent but is factually incorrect, invented, or unsupported by its training data. The model has no internal truth-checker — it predicts plausible token sequences, which can produce fabricated citations, wrong dates, or nonexistent service features. On the AIF-C01 exam, hallucination is the primary motivation for Retrieval-Augmented Generation (RAG), which grounds responses in verified external documents before generation. Amazon Bedrock supports RAG natively through its Knowledge Bases feature, letting applications anchor model output to real source content and reduce unsupported claims.
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