Your RAG Isn't Hallucinating. Your Retrieval Is Lying.

The article highlights a common issue in Retrieval-Augmented Generation (RAG) systems: when they provide incorrect answers, the blame often falls on the large language model (LLM). However, the problem often lies in the flawed retrieval process, which feeds inaccurate or irrelevant information to the LLM. This oversight can lead to user frustration and mistrust. Understanding and improving the retrieval mechanism is crucial for enhancing the overall accuracy and reliability of RAG systems, ensuring they deliver more trustworthy responses.