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在我们将基于 LLM 的库存推荐系统投入生产运行的第一个月末,约 15% 的智能体返回结果是幻觉——听起来言之凿凿,但内容毫无事实依据。 六个月后,幻觉率降到了 1.5%。推动这一数字下降的关键,并非换用更强大的基础模型,而是我们做出的一个决定:不再将大模型相关能力视作应用层的业务问题,而是把它当作平台基础设施来对待。 本文所描述的项目,是一家大型零售企业的库存准确性平台。系统库存记录会因错放、损 ...