LLM Caching Patterns

Overview

Modern LLM applications can reduce costs by 70-95% through intelligent multi-level caching. This skill covers the multi-tier caching architecture (2025 best practice): combining in-memory LRU, Redis semantic caching, and provider-native prompt caching for maximum efficiency.

Real-World Use Cases:

• High-Volume Chatbot: Semantic cache for FAQ variations (80% cache hit rate)
• Code Review Automation: Prompt cache for system instructions (90% savings)
• Content Moderation: L1/L2 cache for repeat content detection
• Multi-Agent Analysis: Hierarchical cache strategy across agents
• Customer Support: Session-aware caching for context continuity

When to use this skill:

• High-volume LLM applications with repeated queries
• Cost-sensitive AI features
• Similar query patterns (e.g., analyzing similar content types)
• Applications requiring sub-second response times
• Multi-agent systems with redundant LLM calls