Prompt caching is a way for an AI provider to reuse the computation from the unchanged beginning of a long request. When an application repeatedly sends the same instructions, tool definitions, policy rules, or reference material, the provider can reuse that already-processed prefix instead of calculating it from scratch each time. The result can be lower input-token cost and faster responses, especially in AI applications that carry a lot of context.

It is not simply saving a copy of the prompt text. As a model reads a prompt, it builds internal key-value, or KV, states that help it attend to earlier information. A cache stores a reusable representation of those states for a matching prefix. The model still processes whatever changes after that point and generates a new answer, so prompt caching does not make outputs fixed or identical.

The practical rule is to put stable material first and changing material later. A product team might keep a long system instruction, reference guide, and tool definitions consistent, then append each customer question or fresh data at the end. Small changes early in the request can prevent a cache match, which is one reason clear dynamic prompting and context design matter when an AI workflow runs at scale.

Providers implement the feature differently. OpenAI supports automatic and explicit cache breakpoints for supported models; Anthropic supports automatic caching or explicit cache_control breakpoints; Google offers implicit context caching on current Gemini models. The details, token thresholds, prices, and cache lifetimes differ by provider and model, so teams should measure cache hits and total cost in the API they actually use rather than assuming every repeated prompt qualifies.

If you want to turn prompting from a one-off chat skill into a dependable part of an AI workflow, the Prompt Engineering for ChatGPT course is a useful next step for learning how to structure instructions, manage context, and get more consistent results.*