How to Improve LLM UX: Speed, Latency & Caching

Large language model (LLM) applications need to prioritize speed to maintain user engagement, as delays longer than a few seconds can disrupt user experience. The article discusses various factors contributing to perceived slowness in LLM apps, including raw latency, context switching, lack of feedback during processing, and delays in delivering usable output. To diagnose and address performance bottlenecks, developers should measure specific metrics such as time to first token (TTFT) and tokens per second (TPS) and identify areas like client handling, network delays, and model processing for optimization. Strategies to reduce real and perceived latency include streaming initial responses quickly, minimizing prompt size, optimizing retrieval processes, and implementing effective caching mechanisms. Additionally, better interaction design, such as acknowledging user input instantly and providing useful partial outputs, can improve perceived speed. Addressing both real and felt delays enhances user experience and can lead to positive business outcomes, such as increased engagement and reduced support needs. Redis is highlighted as a platform that supports low-latency operations and can improve retrieval speed and caching efficiency in LLM applications.

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