If you ship a chatbot, a RAG app, or an AI agent against a large language model, prompt caching is the single optimization that gives you back 50–90% of input cost and 3–10× of time-to-first-token at no quality cost. It isn't a bolt-on trick — it falls directly out of how Transformer attention is defined. Once you understand that, the rest of the stack (TTLs, provider differences, prompt structure) lines up cleanly.
This page is the index to a four-part series that takes you from the theory to a production decision matrix. Pick where to enter based on what you already know.
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