RAGAS: Automated Evaluation of RAG Pipelines
Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines using automated metrics.
Building a RAG pipeline is easy; knowing if it actually works is hard. Ragas (Retrieval Augmented Generation Assessment) is a framework designed to provide quantitative metrics for evaluating the quality of your RAG systems.
The Ragas Metrics
Ragas introduces several key metrics that target different parts of the RAG loop: Faithfulness (checking if the answer is derived from the context), Answer Relevance (checking if the answer actually addresses the query), and Context Precision (checking the quality of the retrieved documents). These metrics provide a clear, data-driven path to optimizing your pipeline.
Model-Based Evaluation
Ragas uses LLMs (like GPT-4) as "judges" to score your pipeline's performance. This allows for automated, scalable evaluation that is much faster and cheaper than human review, enabling teams to iterate rapidly and catch regressions before they hit production.