Prompt caching reuses repeated context so an AI skips reprocessing the same material every time. It’s mechanical: the system notices a large chunk of the prompt matches what it saw before (long instructions, a style guide) and reuses the work it already did, saving time and money. It differs from memory, which brings a saved fact forward, and it never makes the context window bigger; the model still has a limit on what it can see at once.
Imagine a thick client binder on your desk. If a coworker asks ten questions about the same client, you don’t reread the whole binder from page one before each answer. You read it once, keep it open, and use it while answering.
Why you care
You’ll rarely manage this by hand in client work; it lives mostly in API systems and agent frameworks, where a developer puts the stable part of the prompt first: the binder stays fixed while today’s question changes. The lesson carries anyway. Repeated context costs tokens, stable and well-organized instructions are easier for both people and systems to reuse, and rewriting the whole prompt in a slightly different order means the system can reuse less.