What is pre‑training in LLMs, and what is post‑training (fine‑tuning)? Explain the difference.

Pre‑training in LLMs refers to the unsupervised training phase where the model learns language patterns from a large corpus, building a general‑purpose representation. Post‑training (fine‑tuning) is the supervised phase that adapts the pre‑trained model to a specific downstream task using labeled data. The difference lies in scope and objective: pre‑training captures general language understanding, whereas post‑training specializes the model for a particular application, often with fewer parameters and less data.