Semantic caching isn’t just a cost play. It’s how you keep LLM apps fast, consistent, and stable under load.
But high hit rates don’t happen by accident. Our new guide walks you through the levers that actually move performance: cleaning semantic noise, tuning embedding models and similarity thresholds, adding reranking, using metadata filters, setting smarter TTL/eviction policies, pre-warming key entries, and monitoring for drift.
Manvinder Singh, our VP of AI Product Management breaks down 10 practical techniques to optimize your semantic cache, with detailed examples and guidance you can apply using Redis LangCache.