mlsanityData to 14 Oct 2026

Creating a Taxonomy for Retrieval Augmented Generation Applications

arXiv 2408.02854 ↗First posted 5 Aug 2024cs.IRcited by 3

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

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.

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.

Builds on

8 of 53 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Loops On Retrieval Augmented Generation (LoRAG)18 Mar
  2. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge3 Mar
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  5. 2022A Survey on Retrieval-Augmented Text Generation2 Feb

Other references · 3

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  2. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  3. 2024CPR: Retrieval Augmented Generation for Copyright Protectioncited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025T2-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generationcited by 3
  2. 2025EncouRAGe: Evaluating RAG Local, Fast, and Reliablecited by 2
  3. 2026Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QAcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  2. 2025Engineering the RAG Stack: A Comprehensive Review of the Architecture and Trust Frameworks for Retrieval-Augmented Generation Systems
  3. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation System
  4. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  5. 2024RAG Does Not Work for Enterprises
  6. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
  7. 2025Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets
  8. 2024Observations on Building RAG Systems for Technical Documents