Caching context data efficiently is crucial for responsive applications. The video compares client‑side, server‑side, and edge caching, highlighting trade‑offs in latency, consistency, and storage cost. Client‑side caches store recent context in memory or local storage, offering instant access but risk staleness when multiple devices modify data. Server‑side caches, often using Redis or Memcached, centralize state, enabling coordinated invalidation but add network hops. Edge caches at CDNs bring data closer to users, reducing round‑trip time while requiring careful versioning to avoid serving outdated context. The presenter explains cache‑aside, write‑through, and write‑back patterns, recommending cache‑aside for most context reads because it lets the application control freshness. For high‑write scenarios, write‑through ensures updates propagate immediately, though at higher latency. Strategies for invalidation include time‑to‑live (TTL), explicit purge messages via pub/sub, and versioned keys. The video also covers cache warming, pre‑fetching likely context for upcoming requests, and the importance of monitoring hit‑ratio metrics to adjust TTLs. Finally, the speaker warns about cache stampede and suggests using request coalescing or probabilistic early expiration to mitigate spikes when cached entries expire simultaneously.