Reduce Agent Costs with Semantic Caching
A practical LinkedIn Learning course on building semantic caching into LLM-powered agents with RedisVL to reduce repeated model calls, response time, and cost.
The course covers vector-based similarity, metadata filtering and scoped retrieval, connecting a cache to an LLM application, tool-calling support, handling stale data, and evaluating cache performance with metrics such as hit rate and latency.
- Redis
- Agentic AI Development