During pre-training, the model is exposed to a massive, largely unlabeled corpus (for example, public web text). By predicting masked or next-token content, it learns broad statistical patterns of language. This gives the model general linguistic knowledge it can later adapt through fine-tuning. Activities such as domain-specific weight adjustment, deployment packaging, or user-driven evaluation occur in later lifecycle phases, not in pre-training.
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Fundamentals of Generative AI
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