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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Distributed Caching with Redis/Memcached

Implement and manage distributed caches using technologies like Redis or Memcached for high-scale LLM applications.

Distributed Caching: Why

When building high-scale LLM applications, you'll face challenges like high latency and increased API costs. Caching helps, but what happens when your app grows beyond a single server?

Distributed caching spreads your cache across multiple servers. This allows many application instances to share the same cached data, improving performance and consistency.

Scaling LLM Apps

Imagine your LLM app running on several servers. If each server has its own "in-memory" cache, they won't share data. This means:

  • Duplicate work: Server A might re-generate an LLM response already cached by Server B.
  • Inconsistent data: If one server updates its cache, others won't know.
  • Limited capacity: Each server's memory is finite.

Distributed caches solve these by providing a shared, external store.

All lessons in this course

  1. Distributed Caching with Redis/Memcached
  2. Session Management and Context Persistence
  3. Advanced Cache Invalidation Strategies
  4. Semantic Caching for LLM Responses
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