How do you implement caching strategies for RAG systems?

RAG caching reduces latency and cost by reusing previous results at multiple pipeline stages. Cache layers: query cache (exact query match returns cached answer, fastest but lowest hit rate), semantic cache (similar queries share cached results using embedding similarity threshold, higher hit rate), retrieval cache (cache retrieved chunks for repeated or similar queries), and embedding cache (cache computed embeddings for reprocessed documents). Semantic caching implementation: embed incoming query, search cache of previous query embeddings for matches above similarity threshold (typically 0.95), return cached answer if found, otherwise proceed with full RAG pipeline and cache the result. Tools: GPTCache provides a framework for semantic caching with configurable similarity thresholds and eviction policies. Redis with vector search capability (RediSearch) supports both exact and semantic caching. Cache invalidation: time-based TTL (answers become stale after configurable period), event-based (invalidate when source documents are updated), and version-based (invalidate when the underlying model changes). Cache warming: pre-populate cache with answers to frequently asked questions, generate caches during off-peak hours, and implement predictive caching for anticipated queries. Metrics: cache hit rate (target 30-60% for semantic cache), latency reduction (cached responses in under 50ms vs 2-5s for full pipeline), cost reduction (cached responses avoid LLM token costs), and staleness rate (how often cached answers are outdated). Trade-offs: higher similarity threshold reduces false cache hits but lowers hit rate; lower threshold increases hit rate but risks returning semantically different answers. Implement monitoring to track cache quality and adjust thresholds based on user feedback.