Three RAG failures that look like model problems but aren't

Three common failures in Retrieval-Augmented Generation (RAG) systems that are often mistaken for problems with the underlying language model. These issues include the retrieval of irrelevant or conflicting documents, the model's inability to correctly parse or prioritize the retrieved context, and the generation of plausible-sounding but incorrect answers due to gaps in the retrieved information. The article emphasizes that these failures stem from the RAG pipeline's retrieval and integration processes, not from the model itself.