A failure mode where an LLM generates output that is fluent and confident but factually wrong or entirely fabricated. In meeting copilots, hallucinations show up as: invented action items that nobody committed to, misattributed quotes, summaries that contradict the transcript, and made-up participant names. Hallucinations are particularly dangerous in meeting contexts because the output is plausible and the consumer often won't catch the error â a recap that says "Alice agreed to ship by Friday" looks authoritative even if Alice never said it. Defenses against hallucination include grounding (force the model to cite specific transcript chunks), explicit "I don't know" instructions in the system prompt, and post-generation verification where each claim in the output is checked against the source transcript before the recap is delivered.