Why RAG Systems Fail: Retrieval Errors, Not AI Models, Are Usually the Cause

Retrieval-Augmented Generation (RAG) systems commonly produce wrong or incomplete answers not because of model errors, but due to flawed retrieval of source documents. In one tested case, a question about home office expenses returned chunks that referenced the right section but omitted the actual figures, leaving the model unable to provide a complete answer. In a second case, a vague query about a '90-day rule' pulled results from four different documents where the phrase carried entirely different meanings, forcing the model to ask the user for clarification. These failures stem from issues like overly short paragraph-based chunking and the inability of semantic search to distinguish identical phrases used in different contexts. Since retrieval is tunable, developers can address such failures by adjusting chunking strategies and retrieval logic rather than modifying the underlying language model.