10 Common RAG Mistakes We Keep Seeing in Production

Common mistakes in production Retrieval-Augmented Generation (RAG) systems often stem from poor document parsing. Many teams treat documents as simple flat text, which destroys critical structural information like tables and multi-column layouts, leading to ambiguous or incorrect answers. Another frequent error is stuffing entire large documents into the prompt, which is prohibitively expensive and degrades model performance. Instead of tuning downstream parameters like chunk size, the focus should be on implementing a structural parser that preserves the document's inherent organization from the start.