Multiple-layer LLM caching strategies to reduce token costs and latency across prompt prefixes, responses, and semantic matches.

• Supports three caching approaches: Anthropic's native prompt caching for repeated prefixes, response caching for identical or similar queries, and Cache Augmented Generation (CAG) for pre-cached documents
• Includes cache invalidation patterns and guidance on structuring prompts for optimal caching performance
• Highlights critical anti-patterns: caching with high temperature, missing invalidation logic, and over-caching low-value data
• Addresses sharp edges like cache miss latency spikes and prompt prefix changes that break caching effectiveness