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怎么验证 AI 的输出How to Verify AI Output

微型指南 · 约 7 分钟Guide · about 7 min · 引用核验 2026-11-03(仅引用链接与日期) · refs checked 2026-11-03 (reference links and dates only)

AI 会一本正经地编造事实。这份指南讲怎么把「它说的」变成「你能核对的」。AI will state fabricated facts with full confidence. This guide turns "what it said" into "what you can check".

怎么做How to

把输出里的事实拆成一条条可核对的断言:数字、日期、人名、引用。

凡是引用(论文、法条、接口名),去原始出处核对,别信它给的链接标题。

关键数字要求它给出处;给不出处的数字当假设处理。

让它自查会漏掉大半错误,交叉核对(换来源、换工具)才可靠。

越具体的领域(本地法规、自家数据)越要人来核,通用模型没有这些事实。

Break the output into individual checkable claims: numbers, dates, names, citations.

For every citation (paper, statute, API name), verify at the original source — never trust the title it gives you.

Demand sources for key numbers; treat numbers without sources as assumptions.

Self-review catches only a fraction of errors; cross-checking (different source, different tool) is what works.

The more specific the domain (local law, your own data), the more a human must check — general models don't have these facts.

例子与判断Example and interpretation

以下是教学情境,不是本站运行或实测记录。A teaching scenario, not a measured or executed site test.

活动资料只给出周六、14:00、阅览室。把生成通知拆成时间、地点、费用、联系方式四项检查。前三项中若费用没有来源,就删掉或写未知;链接核对也要确认页面内容真的支持该句。An event brief provides Saturday, 14:00 and a reading room. Audit generated time, venue, fees and contacts separately. Remove or mark unsupported fees unknown. For citations, check that the page actually supports the statement.

出处Sources
  • Ji 等《Hallucination in Large Language Models: A Survey》(ACM Computing Surveys 2023)Ji et al., Hallucination Survey (ACM Computing Surveys 2023) ↗
  • Anthropic 官方研究页《Tracing the thoughts of a large language model》Anthropic research: Tracing the thoughts of a large language model ↗