# AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline arXiv: https://arxiv.org/abs/2410.20878 ยท HTML: https://mlsanity.com/paper/2410.20878/ ## Key facts - Cited by 27 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 17 of its 24 references that are in the map. - First posted on arXiv 28 Oct 2024. ## Placed in - Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/) ## Builds on (most cited first, 17 of 17) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023) - [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023) - [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023) - [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016) - [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020) - [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022) - [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020) - [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023) - [Improving Passage Retrieval with Zero-Shot Question Generation](https://mlsanity.com/paper/2204.07496/) (2022) - [Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval](https://mlsanity.com/paper/2108.08787/) (2021) - [Task-aware Retrieval with Instructions](https://mlsanity.com/paper/2211.09260/) (2022) - [An Analysis of Fusion Functions for Hybrid Retrieval](https://mlsanity.com/paper/2210.11934/) (2022) - [Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks](https://mlsanity.com/paper/2407.21059/) (2024) - [SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity](https://mlsanity.com/paper/2401.17072/) (2024) ## Built on by (most cited first, 27 of 27) - [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025) - [A Survey on the Optimization of Large Language Model-based Agents](https://mlsanity.com/paper/2503.12434/) (2025) - [MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search](https://mlsanity.com/paper/2503.20757/) (2025) - [METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation](https://mlsanity.com/paper/2412.10543/) (2024) - [AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding](https://mlsanity.com/paper/2506.13589/) (2025) - [RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliability](https://mlsanity.com/paper/2510.22710/) (2025) - [Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.02464/) (2025) - [Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems](https://mlsanity.com/paper/2502.18635/) (2025) - [syftr: Pareto-Optimal Generative AI](https://mlsanity.com/paper/2505.20266/) (2025) - [RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets](https://mlsanity.com/paper/2511.01386/) (2025) - [C2KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference](https://mlsanity.com/paper/2607.17715/) (2026) - [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025) - [An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation](https://mlsanity.com/paper/2505.03452/) (2025) - [FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.12494/) (2025) - [Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization](https://mlsanity.com/paper/2505.15444/) (2025) - [Domain-Specific Data Generation Framework for RAG Adaptation](https://mlsanity.com/paper/2510.11217/) (2025) - [RAGTurk: Best Practices for Retrieval Augmented Generation in Turkish](https://mlsanity.com/paper/2602.03652/) (2026) - [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026) - [AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines](https://mlsanity.com/paper/2605.02967/) (2026) - [The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.12323/) (2025) - [LogicGaze: Benchmarking Causal Consistency in Visual Narratives via Counterfactual Verification](https://mlsanity.com/paper/2602.00292/) (2026) - [LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG](https://mlsanity.com/paper/2605.06285/) (2026) - [RAISE: RAG Design as an Architecture Search Problem](https://mlsanity.com/paper/2605.30029/) (2026) - [An Agentic Framework Using Rules and LLMs for Embedding and Annotating Descriptive Document Layouts: A Plant Science Use Case](https://mlsanity.com/paper/2608.14587/) (2026) - [UniK: Universal Knowledge Perception for Digital and Physical AI](https://mlsanity.com/paper/2609.23971/) (2026) - [STITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generation](https://mlsanity.com/paper/2609.34127/) (2026) - [Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents](https://mlsanity.com/paper/2610.08452/) (2026) ## Abstract (arXiv) Using LLMs (Large Language Models) in conjunction with external documents has made RAG (Retrieval-Augmented Generation) an essential technology. Numerous techniques and modules for RAG are being researched, but their performance can vary across different datasets. Finding RAG modules that perform well on specific datasets is challenging. In this paper, we propose the AutoRAG framework, which automatically identifies suitable RAG modules for a given dataset. AutoRAG explores and approximates the optimal combination of RAG modules for the dataset. Additionally, we share the results of optimizing a dataset using AutoRAG. All experimental results and data are publicly available and can be accessed through our GitHub repository https://github.com/Marker-Inc-Korea/AutoRAG_ARAGOG_Paper .