Today, instead of discussing a research paper, we review the interview by Jeff Huber, CEO of Chroma, discussing the evolution of AI search and retrieval systems. He champions "context engineering" over the widely used "RAG" (Retrieval-Augmented Generation) concept, arguing that the latter is vague and often misunderstood. Huber highlights the importance of efficiently curating information for Large Language Models (LLMs) to combat "context rot," where model performance degrades with increasing input length. The conversation also touches upon Chroma's distributed database, strategies for code indexing and retrieval, and the significance of generative benchmarking for evaluating AI systems.