Caching AI systems

How to reuse expensive AI computation safely: the layers of an AI system that can be cached (exact responses, semantic matches, retrieval results, embeddings, and provider-side prompt prefixes), how cache-key design and invalidation decide whether a hit is fast or wrong, why caching across users or tenants without a deliberate key is a data leak waiting to happen, and how to measure whether a cache is actually worth its risk. For engineers running AI systems in production who need speed and lower cost without ever serving a confident, stale, or misdirected answer.