The article discusses the limitations of monolithic embeddings in AI, particularly for Retrieval-Augmented Generation (RAG) systems, which require precise, context-specific information rather than averaged representations. It advocates for a chunking approach, where documents are divided into smaller, semantically-focused pieces to improve retrieval accuracy and mimic human research methods. Best practices for effective chunking are also outlined, highlighting the importance of coherent and contextually relevant segments.