Essential metrics and methods to reinforce performance across retrieval, generation, and end-to-end pipelines
Introduction
When we expect of a few of the most typical applications of Generative AI, Retrieval-Augmented Generation (RAG) has indisputably surfaced to turn out to be of probably the most common topics of debate inside this domain. Unlike traditional serps that relied on optimizing retrieval mechanisms using keyword searches to search out relevant information for a given query, RAG goes a step further in generating a well-rounded answer for a given query using the retrieved content.
The figure below illustrates a graphical representation of RAG during which documents of interest are encoded using an embedding model, and are then indexed and stored in a vector store. When a question is submitted, it is mostly embedded in the same manner, followed by two steps (1) the retrieval step that searches for similar documents, after which (2) a generative step that uses the retrieved content to synthesize a response.
