What It Is
Much of each LLM prompt stays the same: system instructions, tool definitions, reference documents, or conversation history. Caching that prefix means it need not be recomputed on every request. When a cached prefix is reused, the model skips repeated work.
Key Points
- Faster responses: less computation per request.
- Lower price: providers charge less for cached tokens.
- Structure matters: put stable content first and variable content last.
- Lifetimes: caches expire, so savings depend on request frequency.
Why It Matters
It can significantly lower cost for agents, document analysis, and support assistants that repeatedly use the same context. Savings depend on how often similar requests arrive within the cache lifetime.
How ClearLeaff Applies It
Prompt caching is a key part of our cost work with Anthropic’s Claude models. We design prompt structure and caching strategy together for responsive, economical applications.