Reframing inevitably in the direction of AI, this also expresses the difference between, for instance, pretraining and retrieval in LLMs. People expect large models to memorize the dataset and are frustrated when it doesn't, even more so when it does but only partially and not thoroughly enough to rely on. But pretraining data : large models as books : people, building a world-sense that is refined and augmented through fine-tuning (for people, specialized training) and retrieval (for people Google searches, Stack Overflow, etc).

People complain about the extent to which we anthropomorphize GPT-class models, but I gain just as much from backporting the analogies that work for LLMs to my own experience.