Many teams struggle to measure the performance of their Retrieval Augmented Generation (RAG) applications due to issues like hallucinations and incorrect data. A metrics-driven approach, using frameworks like RAG Assessment (Ragas), can improve evaluation by quantitatively measuring faithfulness, answer relevancy, context precision, and context recall. This ensures engineers can optimize their systems without relying on anecdotal evidence. Using tools like OpenAI, LangChain, and Redis, developers can establish and test baseline metrics efficiently.
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Let’s start with a simple RAG app.Let’s evaluate our RAG appEach metric shows a different flavor of qualityLet’s ask a different questionWe’ll evaluate our RAG app using a test datasetLet’s run it again with more questions this timeWrapping upSort: