mlsanityData to 7 Oct 2026

The Chronicles of RAG: The Retriever, the Chunk and the Generator

arXiv 2401.07883 ↗First posted 15 Jan 2024cs.LG cs.AI cs.CLcited by 34

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

Abstract (arXiv)

Retrieval Augmented Generation (RAG) has become one of the most popular paradigms for enabling LLMs to access external data, and also as a mechanism for grounding to mitigate against hallucinations. When implementing RAG you can face several challenges like effective integration of retrieval models, efficient representation learning, data diversity, computational efficiency optimization, evaluation, and quality of text generation. Given all these challenges, every day a new technique to improve RAG appears, making it unfeasible to experiment with all combinations for your problem. In this context, this paper presents good practices to implement, optimize, and evaluate RAG for the Brazilian Portuguese language, focusing on the establishment of a simple pipeline for inference and experiments. We explored a diverse set of methods to answer questions about the first Harry Potter book. To generate the answers we used the OpenAI's gpt-4, gpt-4-1106-preview, gpt-3.5-turbo-1106, and Google's Gemini Pro. Focusing on the quality of the retriever, our approach achieved an improvement of MRR@10 by 35.4% compared to the baseline. When optimizing the input size in the application, we observed that it is possible to further enhance it by 2.4%. Finally, we present the complete architecture of the RAG with our recommendations. As result, we moved from a baseline of 57.88% to a maximum relative score of 98.61%.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 21 of its 39 references that are in the map. First posted on arXiv 15 Jan 2024.

Builds on

21 of 39 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  7. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
  8. 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 721
  9. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  10. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  11. 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 266
  12. 2017Efficient Natural Language Response Suggestion for Smart Replycited by 228
8 more
  1. 2019Multi-Stage Document Ranking with BERTcited by 200
  2. 2022Dense Text Retrieval based on Pretrained Language Models: A Surveycited by 136
  3. 2021mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Datasetcited by 116
  4. 2023Retrieval meets Long Context Large Language Modelscited by 114
  5. 2019A Deep Look into Neural Ranking Models for Information Retrievalcited by 79
  6. 2021Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representationscited by 60
  7. 2021Yes, BM25 is a Strong Baseline for Legal Case Retrievalcited by 24
  8. 2022Visconde: Multi-document QA with GPT-3 and Neural Rerankingcited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 21; 2026: 3.

34 papers.

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  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
  3. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Pathscited by 60
  4. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insightscited by 51
  5. 2024Is Semantic Chunking Worth the Computational Cost?cited by 43
  6. 2024Long2RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recallcited by 32
  7. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunicationscited by 26
  8. 2024RAGGED: Towards Informed Design of Scalable and Stable RAG Systemscited by 24
  9. 2025TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoningcited by 22
  10. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  11. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generationcited by 17
  12. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 14
22 more
  1. 2024Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systemscited by 12
  2. 2025DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoningcited by 8
  3. 2025RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practicecited by 6
  4. 2025A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChatcited by 5
  5. 2024RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systemscited by 4
  6. 2025ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generationcited by 3
  7. 2025SEReDeEP: Hallucination Detection in Retrieval-Augmented Models via Semantic Entropy and Context-Parameter Fusioncited by 3
  8. 2025UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generationcited by 3
  9. 2024Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setupcited by 2
  10. 2024Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Taskscited by 2
  11. 2025Can LLMs Replace Humans During Code Chunking?cited by 2
  12. 2025Comparing Knowledge Injection Methods for LLMs in a Low-Resource Regimecited by 2
  13. 2025Enhancing Retrieval-Augmented Generation with Topic-Enriched Embeddings: A Hybrid Approach Integrating Traditional NLP Techniquescited by 2
  14. 2025ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulationcited by 1
  15. 2025TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Constructioncited by 1
  16. 2026Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documentscited by 1
  17. 2025Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Modelscited by 0
  18. 2025Building Specialized Software-Assistant ChatBot with Graph-Based Retrieval-Augmented Generationcited by 0
  19. 2025Breaking It Down: Domain-Aware Semantic Segmentation for Retrieval Augmented Generationcited by 0
  20. 2026Evidence-Grounded Multi-Agent Planning Support for Urban Carbon Governance via RAGcited by 0
  21. 2025Retrieval-Augmented Generation Assistant for Anatomical Pathology Laboratoriescited by 0
  22. 2026Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunkingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
  3. 2024Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
  4. 2024Toward Optimal Search and Retrieval for RAG
  5. 2024R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
  6. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  7. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  8. 2024The Power of Noise: Redefining Retrieval for RAG Systems