Caching skips a full model call by reusing an answer you already computed.
DefinitionWhat it means
Caching in an LLM system stores the result of a previous prompt, tool call, or prompt prefix so that an identical or sufficiently similar future request can be served instantly from the cache instead of paying for a fresh model call.
Why it mattersWhy you should care
Prompt-prefix and semantic caching can cut inference cost and latency dramatically for high-traffic products with repetitive queries, such as customer support or coding assistants, making caching strategy a core part of any production LLMOps cost-optimization plan.
At a glanceSee it
A miss is not a dead end — it calls the model, writes the result back, then the next identical query returns instantly.
Three ways to judge two prompts as the same — exact string, semantic similarity, or a shared prompt prefix — each trading cost against a different failure mode.
Where you see itIn the wild
- Prompt caching features offered by major model providers to cut repeated-context cost.
- Semantic caches in customer support bots serving common questions instantly.
- Architecture discussions on designing a cache invalidation strategy for an AI feature.