Optimizes LLM performance and reduces costs by implementing advanced prompt, response, and Cache Augmented Generation (CAG) strategies.
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This skill provides a comprehensive framework for managing LLM efficiency through sophisticated caching techniques. It focuses on Anthropic’s native prompt caching, Redis-backed response caching, and Cache Augmented Generation (CAG) to minimize latency and slash token costs by up to 90%. By providing implementation patterns for semantic similarity matching and cache invalidation, it ensures that your AI applications remain fast, cost-effective, and accurate even when dealing with large system prompts or frequently repeated queries.
主要功能
01Native Anthropic prompt caching integration
02Redis-based response and KV caching
03Cache Augmented Generation (CAG) patterns
04Semantic similarity and temperature-aware caching
05Advanced cache invalidation and TTL management
0640,341 GitHub stars
使用场景
01Replacing RAG with CAG for stable document corpuses
02Reducing API costs for applications with long system prompts
03Improving response times for frequently asked AI queries
