Client-side caching is usually implemented as a storage optimization layer (TTL, SWR, invalidation rules). In practice it behaves like a decision system under uncertainty. Static strategies fail when data volatility is non-uniform across the same application. This leads to either stale UI or excessive network traffic. This article breaks down: why standard caching approaches plateau where ML improves the system where LLMs actually fit how to design a production-grade decision pipeline Problem: caching is not a storage problem Different data types behave differently: user profiles → low volatility feeds / notifications → high volatility search results → context-dependent volatility partially hydrated UI → unknown volatility The core issue: caching requires a policy decision per request, not a static rule So the real problem is: data → context → decision (cache / revalidate / bypass) Baseline systems (what already exists) 1.…