The magic is in that codify step. LLMs are stateless. If they re-introduce a dependency you explicitly removed yesterday, they'll do it again tomorrow unless you tell them not to. The most common way to close that loop is updating your CLAUDE.md (or equivalent rules file) so the lesson is baked into every future session. A word of caution: the instinct to codify everything into your rules file can backfire (too many instructions is as good as none). The better move is to create a setting where the LLM can easily discover useful context on its own, for example by maintaining an up-to-date docs/ folder (more on this in Level 7).
提升模型精度的关键是：高质量数据积累，及基于实验数据自动迭代的active learning系统。余论介绍道，训练数据主要包括三类：文献与专利数据；与学术机构合作授权的实验室数据；内部实验平台产生的高通量湿实验数据。其中，自有实验平台不仅积累了成功的验证数据，也沉淀了“失败”的负样本数据。这些稀缺的内部反馈，让AI系统在迭代中更加精准。，这一点在51吃瓜中也有详细论述
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据悉，阶跃星辰近期宣布完成 B+轮融资，获得 50 亿元融资，刷新大模型赛道过去 12 个月单笔融资纪录。