# Observations on Building RAG Systems for Technical Documents

arXiv: https://arxiv.org/abs/2404.00657 · HTML: https://mlsanity.com/paper/2404.00657/

## Key facts

- Cited by 8 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 8 of its 11 references that are in the map.
- First posted on arXiv 31 Mar 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, 8 of 8)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Understanding Retrieval Augmentation for Long-Form Question Answering](https://mlsanity.com/paper/2310.12150/) (2023)
- [Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)](https://mlsanity.com/paper/2312.10904/) (2023)
- [Observations on LLMs for Telecom Domain: Capabilities and Limitations](https://mlsanity.com/paper/2305.13102/) (2023)

## Built on by (most cited first, 8 of 8)

- [TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding](https://mlsanity.com/paper/2502.19400/) (2025)
- [Evaluation of RAG Metrics for Question Answering in the Telecom Domain](https://mlsanity.com/paper/2407.12873/) (2024)
- [FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents](https://mlsanity.com/paper/2504.13128/) (2025)
- [Evaluating the Effect of Retrieval Augmentation on Social Biases](https://mlsanity.com/paper/2502.17611/) (2025)
- [Document Intelligence in the Era of Large Language Models: A Survey](https://mlsanity.com/paper/2510.13366/) (2025)
- [A Graph-based Approach for Multi-Modal Question Answering from Flowcharts in Telecom Documents](https://mlsanity.com/paper/2507.22938/) (2025)
- [Balancing Content Size in RAG-Text2SQL System](https://mlsanity.com/paper/2502.15723/) (2025)
- [Evaluation of Table Representations to Answer Questions from Tables in Documents : A Case Study using 3GPP Specifications](https://mlsanity.com/paper/2408.17008/) (2024)

## 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.