According to @DeepLearningAI, a new course teaches developers to build a semantic cache that reuses responses based on meaning rather than exact text to reduce API costs and speed up responses, source: @DeepLearningAI. It details how to measure cache hit rate, precision, and latency to quantify performance for AI agents, source: @DeepLearningAI. The curriculum adds accuracy safeguards via cross-encoders, LLM validation, and fuzzy matching, and shows integration into an agent that improves cost and speed over time, source: @DeepLearningAI. For traders tracking AI infrastructure exposure within crypto, the source highlights practical levers such as cost per request and latency that projects can optimize and report using semantic caching, source: @DeepLearningAI.