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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.

做完例子后怎么检查How to check your practice

先用自己的话解释上面的判断,再检查输入条件,最后回原文验证。说不清原因时回概念;缺日期、字段或权限时补输入;结论无出处时先撤回结论。Explain the example in your own words, inspect input constraints, then verify against the source. Revisit concepts if you cannot explain why; supply missing dates, fields or permissions; withdraw unsupported conclusions.

按这篇主题,先从“结果缺验证”练习查起For this topic, start with unchecked output · 查看全部六个情境与理由See all six scenarios and their reasoning

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 ↗