Full Explanation
Training and Inference are the two distinct phases of an AI model's lifecycle.
Training is the creation phase. It is comparable to writing and printing an encyclopedia. It involves processing massive amounts of data (the internet) to create a static file (the model). This process is computationally expensive, time-consuming, and happens only once per model version. The result is a "frozen" artifact with a specific knowledge cutoff date.
Inference is the usage phase. It is comparable to consulting the encyclopedia. When a user interacts with the model, they are merely sending inputs to be processed by the frozen file. This process is cheap, fast, and does not alter the underlying model.
User inputs, customizations, and "memory" features act as sticky notes attached to the book—context that guides the model's responses for a specific session or user—but they do not rewrite the printed text of the model itself. To change the model's core knowledge, the developers must release a "New Edition" (retrain the model).
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