implement-caching

To reduce LLM API costs, implement prompt caching or semantic caching to store and reuse generated responses for identical or similar queries, significantly cutting operational expenses and latency.

Optimize slow API response times by implementing Redis application-level caching using patterns like cache-aside or read-through, configuring appropriate eviction policies such as LRU or LFU to manage memory.

Cache-aside loads data into the cache on demand, write-through synchronously updates the cache and database together, while write-behind asynchronously updates the database after writing to the cache for faster response times.

Fix stale data in distributed cache architectures by implementing robust cache invalidation patterns that proactively evict or update outdated entries across the topology whenever the underlying database changes.

Yes, you can use SWR or TanStack Query for frontend data caching to manage client-side state, automatically revalidate data, and reduce unnecessary network requests for a faster user experience.