系统工程师讲了怎么优化大语言模型操作，能解决错误留存难题，和普通工具用法不一样。
当专家修正大型语言模型助手的错误时，通常无法长期保存纠正信息，错误类别会再次出现。作者认为这是运营问题而非工具问题，现有机制可持久化纠正但缺乏管理规范。将大语言模型堆栈映射到传统机械系统，识别出配置绑定和验证阶段的缺陷。基于此提出七原则操作系统模型，包含错误循环。
Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering
When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them -- versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules -- does not. Writing as a systems engineer of thirty years, I map the LLM stack onto the machines my profession already operates (frozen silicon, firmware, loadable modules, persistent configuration, volatile memory), identify where the mapping fails (stochastic generation, configuration that binds only probabilistically, no general-purpose retirement (verification) stage by default), and derive from the failures a seven-principle operating discipline with an error loop at its core. Three cases from my own practice illustrate the mechanism, among them a control that silently became the exact harm it was built to prevent. I close with the measurement framework this view implies and the lab study required to test it.