# Creating a Taxonomy for Retrieval Augmented Generation Applications

arXiv: https://arxiv.org/abs/2408.02854 · HTML: https://mlsanity.com/paper/2408.02854/

## Key facts

- Cited by 3 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 53 references that are in the map.
- First posted on arXiv 5 Aug 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)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://mlsanity.com/paper/2402.07630/) (2024)
- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge](https://mlsanity.com/paper/2403.01432/) (2024)
- [CPR: Retrieval Augmented Generation for Copyright Protection](https://mlsanity.com/paper/2403.18920/) (2024)
- [Loops On Retrieval Augmented Generation (LoRAG)](https://mlsanity.com/paper/2403.15450/) (2024)

## Built on by (most cited first, 3 of 3)

- [T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation](https://mlsanity.com/paper/2506.12071/) (2025)
- [EncouRAGe: Evaluating RAG Local, Fast, and Reliable](https://mlsanity.com/paper/2511.04696/) (2025)
- [Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA](https://mlsanity.com/paper/2602.09552/) (2026)

## Abstract (arXiv)

In this research, we develop a taxonomy to conceptualize a comprehensive overview of the constituting characteristics that define retrieval augmented generation (RAG) applications, facilitating the adoption of this technology for different application domains. To the best of our knowledge, no holistic RAG application taxonomies have been developed so far. We employ the method foreign to ACL and thus contribute to the set of methods in the taxonomy creation. It comprises four iterative phases designed to refine and enhance our understanding and presentation of RAG's core dimensions. We have developed a total of five meta-dimensions and sixteen dimensions to comprehensively capture the concept of RAG applications. Thus, the taxonomy can be used to better understand RAG applications and to derive design knowledge for future solutions in specific application domains.