80% of RAG Responses Miss the Mark: Here’s Why

Retrieval-Augmented Generation (RAG) has become a popular method in Generative AI for providing comprehensive answers to queries. Unlike traditional search engines that rely on keyword searches, RAG uses an embedding model to encode documents, index them in a vector store, and then retrieve and generate responses based on similarity to the query. This process involves two main steps: retrieval of similar documents and generation of a synthesized response. Despite its efficiency, RAG has limitations. Studies show that 80% of RAG responses miss the mark due to issues like incomplete or irrelevant information retrieval, which can lead to responses that are not fully accurate or comprehensive. This highlights the need for further refinement in RAG systems to enhance their accuracy and reliability in real-world applications.

Source: towardsdatascience.com