The blog post explores techniques to optimize semantic caching, a process aimed at improving efficiency by reusing previously computed work from large language models (LLMs). It emphasizes that achieving high cache hit rates requires careful management of embedding quality, similarity tuning, time-to-live (TTL) and eviction policies, and operational best practices. Redis LangCache, a managed service for semantic caching, is highlighted as a tool that offers various features to enhance cache effectiveness, such as embedding controls, adaptive TTL/eviction policies, and observability. The blog provides practical techniques for optimization, including removing semantic noise, tuning embedding models, summarizing long contexts, adjusting similarity thresholds, using LLM-based reranking, applying metadata filters, implementing adaptive TTLs, continuous monitoring, pre-warming high-value entries, and combining lexical and semantic caching. These strategies collectively aim to deliver efficient, accurate, and cost-effective retrieval in semantic caches.