Prompt Caching: Cut Latency and Cost on Repeated Context

Prompt caching stores the unchanging part of your prompt—system instructions, tool definitions, long documents, few-shot examples—so the model processes it once and reuses it on later calls. Instead of re-reading 10,000 tokens every turn, you pay full price once, then a fraction on every cache hit.

To get hits, keep cached content byte-identical and put it at the front of the prompt, with the variable user input last. Mark the stable prefix as cacheable, batch related requests inside the cache lifetime (often a few minutes), and avoid editing earlier text mid-conversation, which invalidates everything after the change.

On B4AI, prompt caching is most useful for chat agents with heavy system prompts, RAG pipelines that reuse the same retrieved context, and storyboard or video workflows that share a long style guide across shots. Measure your cache-hit rate, not just raw token counts—a 70% hit rate on a big prefix can cut both your bill and your response time noticeably.