According to Andrew Ng (@AndrewYNg), Redisinc experts @tchutch94 and @ilzhechev have launched a new course on semantic caching for AI agents. This course demonstrates how semantic caching technology can dramatically lower inference costs and reduce response latency for AI applications by recognizing and reusing semantically similar queries, such as refund requests phrased differently. The practical implications include greater scalability for AI-driven customer support, improved user experience, and significant operational cost savings for businesses deploying large language models (LLMs). Semantic caching is rapidly gaining traction as a critical optimization for enterprise AI workflows, especially in high-traffic environments (source: Andrew Ng on Twitter).

According to DeepLearning.AI (@DeepLearningAI), a new course on semantic caching for AI agents is now available, taught by Tyler Hutcherson (@tchutch94) and Iliya Zhechev (@ilzhechev) from RedisInc. The course addresses the common inefficiency of AI agents making redundant API calls for semantically similar queries. Semantic caching enables AI systems to identify and reuse responses for questions with the same meaning, not just identical text, thereby reducing operational costs and significantly improving response times. Participants will learn how to build a semantic cache, measure its effectiveness using hit rate, precision, and latency, and enhance cache accuracy with advanced techniques such as cross-encoders, LLM validation, and fuzzy matching. The curriculum emphasizes practical integration of semantic caching into AI agents, offering a clear business case for organizations aiming to optimize AI workloads and lower infrastructure expenses. This course highlights the growing importance of scalable, cost-effective AI deployment strategies for enterprise adoption (source: DeepLearning.AI, Twitter, Nov 19, 2025).

According to Andrew Ng, the AI Dev 25 x NYC event will feature insights from leading developers at Google, AWS, Vercel, Groq, Mistral AI, and SAP, focusing on practical experiences building production AI systems (source: Andrew Ng, Twitter, Oct 23, 2025). The agenda reveals concrete topics including agentic architecture—detailing the impact of orchestration frameworks and autonomous planning on error handling—context engineering with advanced knowledge graph techniques, and memory systems for complex relational data. Infrastructure discussions will highlight hardware and model scaling bottlenecks, semantic caching strategies for cost and latency reduction, and inference speed's impact on orchestration. Additional sessions cover systematic agent testing, engineering AI governance, regulatory compliance, and context-rich code review tooling. These practical sessions provide actionable business opportunities for enterprises aiming to optimize AI workflows, enhance system reliability, and accelerate AI deployment in production environments.