Caching

Caching in AI applications involves storing and reusing model responses to reduce costs, improve latency, and decrease load on AI services. Effective caching strategies are essential for building cost-efficient AI applications at scale.

Cache strategies for LLM applications include: exact match caching (storing responses for identical prompts), semantic caching (using embeddings to find similar queries), prompt prefix caching (reusing computation for shared prompt prefixes), and response component caching (caching retrievable facts or computations used in responses).

Implementation considerations include: cache invalidation (when does cached data become stale?), cache key design (what defines "the same" request?), storage (Redis, local memory, database), hit rate optimization, and handling dynamic content that shouldn't be cached. Caching can dramatically reduce costs but requires careful design to maintain response quality.