Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909042097520640 |
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| author | Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bresler, Noam Chorev, Shir Tannor, Philip Rokach, Lior |
| author_facet | Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bresler, Noam Chorev, Shir Tannor, Philip Rokach, Lior |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14488 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deepchecks: Evaluating Retrieval-Augmented Generation (RAG) Gerner, Assaf Madvil, Netta Barak, Nadav Zaikman, Alex Liberman, Jonatan Hamra, Liron Brazilay, Rotem Tsadok, Shay Friedman, Yaron Harow, Neal Bresler, Noam Chorev, Shir Tannor, Philip Rokach, Lior Artificial Intelligence 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. |
| title | Deepchecks: Evaluating Retrieval-Augmented Generation (RAG) |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.14488 |