mlsanityData to 7 Oct 2026

Observations on Building RAG Systems for Technical Documents

arXiv 2404.00657 ↗First posted 31 Mar 2024cs.LG cs.AI cs.CLcited by 8

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

Abstract (arXiv)

Retrieval augmented generation (RAG) for technical documents creates challenges as embeddings do not often capture domain information. We review prior art for important factors affecting RAG and perform experiments to highlight best practices and potential challenges to build RAG systems for technical documents.

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 8 of its 11 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

8 of 11 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 7

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  4. 2023Benchmarking Large Language Models in Retrieval-Augmented Generationcited by 307
  5. 2023Understanding Retrieval Augmentation for Long-Form Question Answeringcited by 45
  6. 2023Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)cited by 14
  7. 2023Observations on LLMs for Telecom Domain: Capabilities and Limitationscited by 11

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 6.

8 papers.

  1. 2025TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understandingcited by 20
  2. 2024Evaluation of RAG Metrics for Question Answering in the Telecom Domaincited by 19
  3. 2025FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documentscited by 15
  4. 2025Evaluating the Effect of Retrieval Augmentation on Social Biasescited by 7
  5. 2025Document Intelligence in the Era of Large Language Models: A Surveycited by 5
  6. 2025A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documentscited by 3
  7. 2025Balancing Content Size in RAG-Text2SQL Systemcited by 2
  8. 2024Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specificationscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System
  2. 2025Enhancing Technical Documents Retrieval for RAG
  3. 2024Enhanced document retrieval with topic embeddings
  4. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  7. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  8. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey