Prompt Caching: Faster, Cheaper AI Without Rewriting Your Prompts

Prompt caching stores the tokens a model has already processed—system instructions, style guides, reference documents—so they don't have to be re-read on every call. The model only pays full price to process the new part of each request, typically the user's latest message. For long, stable prefixes this can cut input cost by up to 90% and noticeably reduce time-to-first-token.

The trick is structuring prompts so the cached portion stays byte-identical and comes first: put your fixed system prompt, character sheets, or storyboard context at the top, and append the variable user input at the end. Any change to the prefix invalidates the cache, so avoid injecting timestamps or per-request IDs early. Caches also expire after a few minutes of inactivity, which makes them ideal for bursty, multi-turn sessions rather than one-off queries.

On CinderHub, this matters most for iterative workflows—refining a chat persona, regenerating frames from the same storyboard, or looping over image variations that share a long style prompt. Keep the shared scaffolding constant across calls and let only the creative delta change. You get quicker responses and a smaller bill without touching the quality of the output.