Get better RAG responses with Ragas

The text discusses measuring Retrieval Augmented Generation (RAG) apps and introduces the RAG Assessment (Ragas) framework, which consists of four primary metrics: faithfulness, answer relevancy, context precision, and context recall. These metrics help developers evaluate their GenAI apps by measuring performance rather than guessing. The text also provides a code example using LangChain, Redis, and OpenAI to create a simple RAG app for answering questions about financial documents. Additionally, the author explains how to generate test sets with the Ragas library and emphasizes the importance of creating challenging test sets to evaluate the performance of RAG apps accurately.