Why Chunking Is the Biggest Mistake in RAG Systems

Retrieval-Augmented Generation (RAG) has become the default architecture for building AI-powered document intelligence systems. Most implementations follow the same pattern: Split documents into chunks Convert chunks into embeddings Store them in a vector database Retrieve the most similar chunks Send them to an LLM to generate answers This pipeline works reasonably well for simple text. However, when applied to structured documents like clinical records, chunking can introduce serious problems.