The current approach to large language models (LLMs) often anthropomorphizes them, treating them like digital friends, which leads to misunderstandings and disappointment when they don't behave as expected. A more effective framework is to view LLMs as state machines, focusing on their engineering aspects rather than social simulation. This involves understanding the components such as the Substrate (the neural network), Anchor (the system prompt), and Peripherals (input/output systems) that work together to process information and execute commands. By adopting this modular and technical perspective, users can better manage and utilize LLMs as reliable tools rather than unpredictable companions. This matters because it shifts the focus from emotional interaction to practical application, enhancing the reliability and efficiency of LLMs in various tasks.
