Ragas is a framework designed to evaluate the performance of retrieval-augmented generation (RAG) pipelines, addressing the challenge of assessing both retrieval and generation components in these systems. It provides structured metrics such as faithfulness, answer relevance and context utilization which help evaluate the effectiveness of RAG-based systems. Our developers found it useful for running periodic evaluations to fine-tune parameters like top-k retrievals and embedding models. Some teams have integrated Ragas into pipelines that run daily, whenever the prompt template or the model changes. While its metrics offer solid insights, we’re concerned that the framework may not capture all the nuances and intricate interactions of complex RAG pipelines, and we recommend considering additional evaluation frameworks. Nevertheless, Ragas stands out for its ability to streamline RAG assessment in production environments, offering valuable data-driven improvements.
