A polished legal brief containing six flawless citations turned out to be built on three made-up cases, a vivid example of AI "hallucination" and how confidence can masquerade as truth. This episode explores why large language models invent plausible-sounding facts, why fluent answers mislead even experienced professionals, and how those fabrications can propagate and cause real harm.

Our four-expert panel traces the root causes (pattern completion, not retrieval), maps hallucination types (fabrication, conflation, outdatedness, overgeneralization, context loss), and shows when risks spike: obscure topics, demands for specificity, leading prompts, and long interactions.

Practical defenses are offered: retrieval-grounding, enforced sourcing and citation verification, consistency checks, calibrated uncertainty, human checkpoints for high-risk uses, audit logs, and governance that ties accountability to people and processes. The takeaway: treat AI output as a draft, verify before relying, and build layered safeguards so speed never replaces accuracy.

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