A marina's 200-page slip-policy binder will not fit in one model sitting. Staff split it into overlapping passages before anyone builds a prompt or a retrieval index. They are not training a new model and are not turning the pages into meaning-vectors yet. Which step is this?
Select an answer to reveal the explanation.
Short Explanation
Think of tearing a 200-page binder into overlapping photocopies so each sitting can hold a piece. That is chunking, before anyone embeds or prompts. Fine-tuning would rewrite the model's brain, and temperature will not shrink the binder.
Full Explanation
Chunking cuts long source text into workable pieces, often with overlap, before later retrieval or prompting. Embedding would turn pieces into meaning vectors, which staff have not done yet. Fine-tuning changes model weights and is not this split. Temperature is a generation setting and does not make a 200-page binder fit.