This video explores the crucial, yet often overlooked, dimension of time in Retrieval Augmented Generation (RAG) and knowledge graphs. Daniel Davis discusses how time impacts data validity, introduces a framework for classifying data as observations, assertions, or facts, and advocates for building robust, modular AI systems with specialized tools over generalist solutions. The conversation highlights the challenges of managing temporal dependencies, the limitations of current LLM-driven knowledge graph approaches, and the importance of solid infrastructure and simplified designs in the AI landscape.