LLM Prompt Caching
Optimizes LLM performance and reduces costs using Anthropic prompt caching, response caching, and Cache Augmented Generation (CAG) strategies.
This skill provides expert guidance on implementing advanced caching strategies for Large Language Models, specifically focusing on Claude's native prompt caching capabilities and external response caching patterns like Redis. It enables developers to significantly reduce API costs by up to 90% and decrease latency by up to 2x through systematic prefix management, semantic similarity caching, and the CAG (Cache Augmented Generation) pattern for stable document sets. By following these implementation patterns, developers can handle high-frequency queries more efficiently while maintaining data freshness through robust invalidation logic.