Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)

TL;DR

Deepchecks is a comprehensive evaluation framework designed specifically for Retrieval-Augmented Generation systems, addressing their unique challenges in assessing reliability, relevance, and user satisfaction.

Contribution

It introduces a multi-faceted evaluation approach, including root cause analysis and production monitoring, tailored for RAG applications.

Findings

Provides a robust foundation for RAG system assessment

Addresses evaluation challenges due to stochastic outputs

Ensures alignment with application-specific requirements

Abstract

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.