prompt-caching

You can reduce LLM costs and latency with prompt caching by implementing multi-level caching for prefixes, responses, and semantic matches across iterative conversations and RAG-like workflows. This avoids redundant token processing for common prompts.

Cache Augmented Generation (CAG) is a pattern that uses cached context for faster retrieval instead of standard dynamic retrieval. It leverages prompt caching to provide pre-loaded context, reducing retrieval overhead and latency in LLM workflows.

You handle cache invalidation for LLM response caches by applying cache invalidation patterns that detect semantic changes in queries. This ensures outdated responses are cleared while preserving valid prefix caches for ongoing iterative conversations.

Yes, you can apply Anthropic prompt caching alongside response caches and semantic matches. The approach supports multi-level caching to manage common prefixes, exact responses, and semantically similar queries within your LLM application.

A key limitation of prompt caching in RAG-like workflows is managing cache invalidation when underlying context changes. If semantic matches are not invalidated properly, the LLM may retrieve outdated cached responses instead of current information.