RAG vs. CAG - Solving Knowledge Gaps in AI Models

This 16-minute video from IBM explores how Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) solve knowledge gaps in AI models. Martin Keen explains the limitations of traditional AI models when answering time-sensitive questions like recent Oscar winners, then demonstrates how RAG and CAG methodologies overcome these challenges. Learn about the comparative strengths of both approaches in real-time information retrieval, scalability, and creating efficient AI workflows. Discover practical implementation strategies for enhancing AI systems with external knowledge sources to build smarter, more responsive applications. The video includes resources for becoming a certified watsonx Generative AI Engineer with a special discount code for exam registration.