RAG in Generative AI: Demystifying Retrieval-Augmented Generation

This 45-minute academic lecture from Pragmatic AI Labs demystifies Retrieval-Augmented Generation (RAG) technology while challenging prevailing AI hype. Learn why current generative AI functions more as advanced search rather than true intelligence, and discover how RAG grounds models in verified data to reduce hallucinations. The lecture explains vector databases as collaborative filtering algorithms operating in multidimensional space, highlights AWS Bedrock's unified API access to multiple AI models, and discusses implementation challenges including extensive data curation requirements. Explore how quality control principles from Toyota Way and DevOps apply to AI implementation, and understand why so-called "agents" are essentially constrained scripts rather than intelligent entities. The lecture serves as a foundation for upcoming coding implementations of RAG technology and provides practical considerations for organizational data curation.