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Governing hallucination: turning uncertainty into controllable product design
Hallucination can't be fully eliminated, but retrieval, constraints, and human-in-the-loop can keep its impact within an acceptable range.
LLMs 'confidently fabricate', and in high-risk settings like health, law, and finance, hallucination bears directly on whether users are harmed and whether you're liable. Expecting a model never to err is unrealistic.
Workable governance is layered: ground answers on trusted sources via retrieval, catch obvious errors with constraints and validation, route high-risk questions to humans or authoritative channels, and clearly mark AI generation and its limits in the UI.
The pragmatic approach is to size guardrail strength to each scenario's risk and keep handling and correction records. The goal isn't to eliminate hallucination but to keep its impact controllable and accountable.