# A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions

arXiv: https://arxiv.org/abs/2410.12837 · HTML: https://mlsanity.com/paper/2410.12837/

## Key facts

- Cited by 70 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 11 of its 11 references that are in the map.
- First posted on arXiv 3 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, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Retrieval-Augmented Multimodal Language Modeling](https://mlsanity.com/paper/2211.12561/) (2022)
- [Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval](https://mlsanity.com/paper/2312.15503/) (2023)
- [Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions](https://mlsanity.com/paper/2308.14263/) (2023)
- [TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction](https://mlsanity.com/paper/2307.04642/) (2023)
- ["Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2312.11361/) (2023)
- [LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.08597/) (2024)

## Built on by (most cited first, 30 of 70)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization](https://mlsanity.com/paper/2505.15107/) (2025)
- [Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook](https://mlsanity.com/paper/2503.18016/) (2025)
- [Solving Inequality Proofs with Large Language Models](https://mlsanity.com/paper/2506.07927/) (2025)
- [Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning](https://mlsanity.com/paper/2505.24478/) (2025)
- [RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliability](https://mlsanity.com/paper/2510.22710/) (2025)
- [Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.19754/) (2025)
- [Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2503.06254/) (2025)
- [CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models](https://mlsanity.com/paper/2504.13534/) (2025)
- [GLARE: Agentic Reasoning for Legal Judgment Prediction](https://mlsanity.com/paper/2508.16383/) (2025)
- [Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights](https://mlsanity.com/paper/2511.06738/) (2025)
- [mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs](https://mlsanity.com/paper/2505.11180/) (2025)
- [Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents](https://mlsanity.com/paper/2505.12065/) (2025)
- [CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter](https://mlsanity.com/paper/2501.15098/) (2025)
- [Less LLM, More Documents: Searching for Improved RAG](https://mlsanity.com/paper/2510.02657/) (2025)
- [Generative AI Enhanced Financial Risk Management Information Retrieval](https://mlsanity.com/paper/2504.06293/) (2025)
- [Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation](https://mlsanity.com/paper/2506.20869/) (2025)
- [ILRe: Intermediate Layer Retrieval for Context Compression in Causal Language Models](https://mlsanity.com/paper/2508.17892/) (2025)
- [Comprehensive Framework for Evaluating Conversational AI Chatbots](https://mlsanity.com/paper/2502.06105/) (2025)
- [Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models](https://mlsanity.com/paper/2503.16581/) (2025)
- [ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models](https://mlsanity.com/paper/2506.20915/) (2025)
- [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026)
- [LURE-RAG: Lightweight Utility-driven Reranking for Efficient RAG](https://mlsanity.com/paper/2601.19535/) (2026)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [Cognitive-Aligned Document Selection for Retrieval-augmented Generation](https://mlsanity.com/paper/2502.11770/) (2025)
- [FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data](https://mlsanity.com/paper/2502.18471/) (2025)
- [Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification](https://mlsanity.com/paper/2503.10886/) (2025)
- [Agent-Enhanced Large Language Models for Researching Political Institutions](https://mlsanity.com/paper/2503.13524/) (2025)
- [Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework](https://mlsanity.com/paper/2504.00338/) (2025)
- [Less Context, Same Performance: A RAG Framework for Resource-Efficient LLM-Based Clinical NLP](https://mlsanity.com/paper/2505.20320/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive study of Retrieval-Augmented Generation (RAG), tracing its evolution from foundational concepts to the current state of the art. RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs, addressing key limitations of LLMs. The study explores the basic architecture of RAG, focusing on how retrieval and generation are integrated to handle knowledge-intensive tasks. A detailed review of the significant technological advancements in RAG is provided, including key innovations in retrieval-augmented language models and applications across various domains such as question-answering, summarization, and knowledge-based tasks. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Furthermore, the paper examines ongoing challenges such as scalability, bias, and ethical concerns in deployment. Future research directions are proposed, focusing on improving the robustness of RAG models, expanding the scope of application of RAG models, and addressing societal implications. This survey aims to serve as a foundational resource for researchers and practitioners in understanding the potential of RAG and its trajectory in natural language processing.