RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems

TL;DR

RAGExplorer is a visual analytics tool designed to help developers systematically compare, diagnose, and optimize Retrieval-Augmented Generation (RAG) systems by providing detailed insights into configuration choices and failure cases.

Contribution

It introduces a novel visual analytics system that guides users through macro-to-micro analysis of RAG configurations, aiding in performance understanding and troubleshooting.

Findings

Effective in identifying optimal RAG configurations

Helps diagnose causes of failure in RAG systems

Empowers developers to improve RAG performance

Abstract

The advent of Retrieval-Augmented Generation (RAG) has significantly enhanced the ability of Large Language Models (LLMs) to produce factually accurate and up-to-date responses. However, the performance of a RAG system is not determined by a single component but emerges from a complex interplay of modular choices, such as embedding models and retrieval algorithms. This creates a vast and often opaque configuration space, making it challenging for developers to understand performance trade-offs and identify optimal designs. To address this challenge, we present RAGExplorer, a visual analytics system for the systematic comparison and diagnosis of RAG configurations. RAGExplorer guides users through a seamless macro-to-micro analytical workflow. Initially, it empowers developers to survey the performance landscape across numerous configurations, allowing for a high-level understanding of which design choices are most effective. For a deeper analysis, the system enables users to drill down into individual failure cases, investigate how differences in retrieved information contribute to errors, and interactively test hypotheses by manipulating the provided context to observe the resulting impact on the generated answer. We demonstrate the effectiveness of RAGExplorer through detailed case studies and user studies, validating its ability to empower developers in navigating the complex RAG design space. Our code and user guide are publicly available at https://github.com/Thymezzz/RAGExplorer.