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How tokenization works and why LLMs measure text in tokens, not characters.

How input/output token pricing works and what actually drives your bill.

What counts toward a model's context window and how usage adds up.

What embeddings are, how embedding pricing works, and how costs scale.

How chunk size and overlap affect retrieval quality and embedding cost.

What each classification metric measures and when to prioritize which one.