mlsanityData to 14 Oct 2026

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

arXiv 2601.12991 ↗First posted 19 Jan 2026cs.HC cs.CLcited by 2

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 61 of its 76 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

61 of 76 references.

Closest ancestors · 23

Same direction, newest first

  1. 2025RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation8 Aug
  2. 2025RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines18 May
  3. 2025RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines18 Apr
  4. 2025More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG6 Mar
  5. 2025U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack1 Mar
  6. 2025Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems25 Feb
  7. 2024Unanswerability Evaluation for Retrieval Augmented Generation16 Dec
  8. 2024RAGViz: Diagnose and Visualize Retrieval-Augmented Generation4 Nov
  9. 2024A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation11 Oct
  10. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions3 Oct
  11. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation15 Aug
  12. 2024Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks26 Jul
11 more
  1. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems25 Jun
  2. 2024Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata19 Jun
  3. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research22 May
  4. 2024Evaluation of Retrieval-Augmented Generation: A Survey13 May
  5. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models10 May
  6. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generation21 Apr
  7. 2024Corrective Retrieval Augmented Generation29 Jan
  8. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan
  9. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System11 Jan
  10. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  11. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 38

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  6. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  7. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  8. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  9. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 184
  10. 2018Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontierscited by 117
  11. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  12. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
26 more
  1. 2018Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Modelscited by 69
  2. 2017ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Modelscited by 56
  3. 2023AttentionViz: A Global View of Transformer Attentioncited by 50
  4. 2020Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Modelscited by 49
  5. 2019Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiplescited by 38
  6. 2017Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspectivecited by 36
  7. 2023Foundation Models Meet Visualizations: Challenges and Opportunitiescited by 31
  8. 2020Boxer: Interactive Comparison of Classifier Resultscited by 28
  9. 2021Dodrio: Exploring Transformer Models with Interactive Visualizationcited by 28
  10. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generationcited by 28
  11. 2024ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systemscited by 23
  12. 2024CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGcited by 22
  13. 2024Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generationcited by 21
  14. 2021LMdiff: A Visual Diff Tool to Compare Language Modelscited by 17
  15. 2024KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integrationcited by 15
  16. 2024Agentic Retrieval-Augmented Generation for Time Series Analysiscited by 14
  17. 2024Evaluating the Retrieval Component in LLM-Based Question Answering Systemscited by 11
  18. 2022Learning-From-Disagreement: A Model Comparison and Visual Analytics Frameworkcited by 10
  19. 2023The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inferencecited by 9
  20. 2023Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answeringcited by 9
  21. 2025CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Modelscited by 8
  22. 2024KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Promptscited by 7
  23. 2025Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generationcited by 6
  24. 2024Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experimentscited by 4
  25. 2024HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agentscited by 2
  26. 2025XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026GuidedRAG: Semantic Steering of Retrieval-Augmented Generationcited by 0
  2. 2026FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Explorationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Configuring RAG Systems: What Breaks, Where Originates, and How to Fix
  2. 2024InspectorRAGet: An Introspection Platform for RAG Evaluation
  3. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  4. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  5. 2025RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation
  6. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  7. 2026RAG-Stack: Co-Optimizing RAG Serving Performance and Quality
  8. 2026Diagnosing and Repairing Factual Errors in RAG under Budget Constraints