Clustered Retrieved Augmented Generation (CRAG)
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
Providing external knowledge to Large Language Models (LLMs) is a key point for using these models in real-world applications for several reasons, such as incorporating up-to-date content in a real-time manner, providing access to domain-specific knowledge, and contributing to hallucination prevention. The vector database-based Retrieval Augmented Generation (RAG) approach has been widely adopted to this end. Thus, any part of external knowledge can be retrieved and provided to some LLM as the input context. Despite RAG approach's success, it still might be unfeasible for some applications, because the context retrieved can demand a longer context window than the size supported by LLM. Even when the context retrieved fits into the context window size, the number of tokens might be expressive and, consequently, impact costs and processing time, becoming impractical for most applications. To address these, we propose CRAG, a novel approach able to effectively reduce the number of prompting tokens without degrading the quality of the response generated compared to a solution using RAG. Through our experiments, we show that CRAG can reduce the number of tokens by at least 46%, achieving more than 90% in some cases, compared to RAG. Moreover, the number of tokens with CRAG does not increase considerably when the number of reviews analyzed is higher, unlike RAG, where the number of tokens is almost 9x higher when there are 75 reviews compared to 4 reviews.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 3 of its 8 references that are in the map. First posted on arXiv 24 May 2024.
Builds on
3 of 8 references.
References · 3
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
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- 2023Retrieval-Augmented Generation for Large Language Models: A Survey
- 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
- 2024Long Context RAG Performance of Large Language Models
- 2024Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
- 2024Corrective Retrieval Augmented Generation
- 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
- 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
- 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models