Large Language Models (LLMs) augmented with Retrieval-Augmented Generation (RAG) techniques are revolutionizing applications across multiple domains, such as healthcare, finance, and customer service. Despite their potential, evaluating RAG systems remains a complex challenge due to the stochastic nature of generated outputs and the intricate interplay between retrieval and generation components. This paper introduces Deepchecks, a comprehensive framework tailored for evaluating RAG applications. Deepchecks' evaluation framework addresses RAG applications evaluation through a multi-faceted approach, root cause analysis and production monitoring. By ensuring alignment with application-specific requirements, Deepchecks framework provides a robust foundation for assessing reliability, relevance, and user satisfaction in RAG systems.
@article{arxiv.2605.14488,
title = {Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)},
author = {Assaf Gerner and Netta Madvil and Nadav Barak and Alex Zaikman and Jonatan Liberman and Liron Hamra and Rotem Brazilay and Shay Tsadok and Yaron Friedman and Neal Harow and Noam Bresler and Shir Chorev and Philip Tannor and Lior Rokach},
journal= {arXiv preprint arXiv:2605.14488},
year = {2026}
}