The article discusses the limitations of using monolithic embeddings for document representation in AI, particularly in the context of Retrieval-Augmented Generation (RAG). It advocates for a chunking approach, where documents are broken down into smaller, semantically-focused pieces to enhance precision in information retrieval. The article also outlines various strategies for effective chunking to optimize AI performance.