The Latency Challenge
AI systems often add context retrieval to already latency-sensitive request paths. Adding 100ms of context lookup to a 200ms LLM call significantly degrades user experience. Achieving sub-millisecond retrieval enables context-rich AI without perceptible delay.
Caching Strategies
Multi-Tier Caching
Implement caching at multiple levels: in-process LRU caches for hot context, distributed caches (Redis/Memcached) for shared access, and read replicas for database-level caching. Each tier trades freshness for speed.
Precomputed Context
For predictable access patterns, precompute and cache complete context bundles. User logs in? Eager-load their full context profile. Session starts? Pre-warm caches with likely-needed context.
Cache Invalidation
The classic hard problem. Implement event-driven invalidation when context changes, set appropriate TTLs as backstops, and design for graceful handling of stale reads in non-critical paths.
Index Optimization
Design indexes for your actual query patterns. Composite indexes for common filter combinations, covering indexes to avoid table lookups, and partial indexes for frequently-filtered subsets.
Data Locality
Place context close to compute. Regional replicas for geographic distribution, edge caching for global users, and consider embedding frequently-accessed context directly in application instances.
