mlsanityData to 7 Oct 2026

Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report

arXiv 2410.15944 ↗First posted 21 Oct 2024cs.SE cs.AI cs.IRcited by 8

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

Abstract (arXiv)

This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. This approach has the potential to redefine how we interact with and augment both structured and unstructured knowledge in generative models to enhance transparency, accuracy, and contextuality of responses. The paper details the end-to-end pipeline, from data collection, preprocessing, to retrieval indexing and response generation, highlighting technical challenges and practical solutions. We aim to offer insights to researchers and practitioners developing similar systems using two distinct approaches: OpenAI's Assistant API with GPT Series and Llama's open-source models. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors where domain-specific knowledge and real-time information retrieval is important. The Python code used in this work is also available at: https://github.com/GPT-Laboratory/RAG-LLM-Development-Guidebook-from-PDFs.

Key facts. Cited by 8 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 2 of its 9 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

2 of 9 references.

References · 2

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2021Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfallscited by 26

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  2. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluationcited by 3
  3. 2025SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controllingcited by 3
  4. 2025Balancing Content Size in RAG-Text2SQL Systemcited by 2
  5. 2026ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Predictioncited by 2
  6. 2025Optimizing Retrieval-Augmented Generation for Electrical Engineering: A Case Study on ABB Circuit Breakerscited by 0
  7. 2026Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Managementcited by 0
  8. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  2. 2024Deploying Large Language Models With Retrieval Augmented Generation
  3. 2024The Power of Noise: Redefining Retrieval for RAG Systems
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture
  6. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  7. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  8. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation