caching▌

Implement multi-level caching strategies using Redis, Memcached, and database-level caching. Covers cache invalidation, TTL strategies, and cache warming patterns.

You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.

Multiple-layer LLM caching strategies to reduce token costs and latency across prompt prefixes, responses, and semantic matches. \n \n 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 \n Includes cache invalidation patterns and guidance on structuring prompts for optimal caching performance \n Highlights critical anti-patterns: caching with h

Testing Spring Cache annotations without external infrastructure using in-memory cache managers. \n \n Covers testing @Cacheable , @CacheEvict , and @CachePut annotations with patterns for verifying cache hits, misses, and invalidation \n Uses ConcurrentMapCacheManager for fast, isolated unit tests instead of Redis or other external caches \n Includes verification strategies via mock call counts, conditional caching with unless and condition parameters, and custom cache key generation with SpEL

Comprehensive caching and performance optimization for Flutter apps across multiple data types and platforms. \n \n Provides decision logic to select appropriate caching mechanisms: in-memory, shared_preferences for key-value pairs, SQLite for relational data, file system for large blobs, and image caching for network images \n Includes stream-based offline-first repository pattern that yields cached data first, then fetches and updates from the network \n Covers Android FlutterEngine pre-warmin

Production patterns for Turborepo build optimization.

Implement effective caching strategies to improve application performance, reduce latency, and decrease load on backend systems.

Turborepo configuration for efficient monorepo builds with local and remote caching strategies. \n \n Configure pipeline tasks with dependency graphs, output caching, and environment-specific inputs to optimize build performance across workspaces \n Supports local caching, Vercel remote caching, and self-hosted cache servers with filtering to build only affected packages \n Includes task-level configuration for persistent processes (dev servers), cache exclusions, and package-specific pipeline o

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