Ragas: Automated Evaluation of Retrieval Augmented Generation
This addresses the problem of efficient and automated evaluation for RAG systems, which is crucial for developers and researchers due to the rapid adoption of LLMs, though it is incremental as it builds on existing RAG concepts.
The paper tackles the challenge of evaluating Retrieval Augmented Generation (RAG) pipelines by introducing Ragas, a framework that provides reference-free metrics to assess retrieval relevance, LLM faithfulness, and generation quality without human annotations, enabling faster evaluation cycles.
We introduce Ragas (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are composed of a retrieval and an LLM based generation module, and provide LLMs with knowledge from a reference textual database, which enables them to act as a natural language layer between a user and textual databases, reducing the risk of hallucinations. Evaluating RAG architectures is, however, challenging because there are several dimensions to consider: the ability of the retrieval system to identify relevant and focused context passages, the ability of the LLM to exploit such passages in a faithful way, or the quality of the generation itself. With Ragas, we put forward a suite of metrics which can be used to evaluate these different dimensions \textit{without having to rely on ground truth human annotations}. We posit that such a framework can crucially contribute to faster evaluation cycles of RAG architectures, which is especially important given the fast adoption of LLMs.