query result caching

**Query result caching** is the **cache strategy that stores final or near-final ranked retrieval results for repeated queries** - it can provide major latency gains when workloads include frequent query repetition. **What Is Query result caching?** - **Definition**: Persisting top-k retrieval outputs keyed by normalized query and filter state. - **Stored Artifacts**: Includes ranked IDs, scores, metadata, and optional reranked ordering. - **Validity Scope**: Cache entries are valid only for matching index version and policy context. - **Deployment Pattern**: Often implemented in fast in-memory stores near retrieval services. **Why Query result caching Matters** - **Fast Reuse**: Popular queries can skip expensive retrieval and reranking operations. - **Infrastructure Relief**: Reduces repeated load on vector databases and search clusters. - **Tail Latency Control**: High cache hit rates stabilize p95 and p99 response times. - **Cost Optimization**: Lowers compute usage for repeated business and support questions. - **User Experience**: Improves responsiveness in chat sessions with recurring intents. **How It Is Used in Practice** - **Normalization Layer**: Canonicalize spelling, casing, and whitespace before cache lookup. - **Version Binding**: Tie entries to index snapshot IDs to prevent stale retrieval reuse. - **Selective Caching**: Prioritize high-frequency queries and bypass cache for low-repeat traffic. Query result caching is **one of the highest ROI optimizations for repetitive retrieval traffic** - correct keying and invalidation rules are critical to prevent stale evidence reuse.