Expired50% confidenceFactExact time
After current large models complete training, their weights are frozen, and they simulate awareness of new information through context windows and RAG, while the model itself does not truly change.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/17/2026
First Seen
Valid until: 10/15/2026(expired)
Sources
Related Claims
UnverifiedCurrent large models are essentially static—once training is complete, the weights are frozen and knowledge remains at the training cutoff date.81% similarUnverifiedAn increasing number of real-world scenarios involve calling pre-trained large models, designing prompts, building RAG systems, and embedding models into products.70% similarUnverified当任务要求访问模型训练截止日期之后的信息、精确数值计算或处理外部文件时，提示词工程无济于事，这是工具调用和RAG出现的根本原因69% similarUnverified大模型在训练时学习的是静态知识模式，难以感知真实业务场景中界面元素属性值动态变化的运行时不确定性67% similarUnverifiedCompared to retraining a model (which can cost millions of dollars), RAG only requires updating the knowledge base to enable the model to learn new knowledge, at a nearly negligible cost.67% similar
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