arXiv:2606.25656v1 Announce Type: new Abstract: As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to adv
The proliferation of RAG-based systems has necessitated a deeper understanding and optimization of their various architectural patterns, especially as 'advanced' variants like GraphRAG and Agentic RAG emerge.
This research provides a framework for evaluating and comparing different RAG scenarios, which is crucial for organizations seeking to implement or upgrade their knowledge retrieval systems efficiently and effectively.
The explicit comparison and benchmarking of various RAG architectures, inclusive of their practical implementation and evaluation against semi-structured knowledge bases, offers a clearer path for developers and strategists.
- · AI developers and researchers
- · Enterprises implementing advanced RAG solutions
- · Data architects
- · Companies relying on outdated RAG implementations
- · Developers unfamiliar with advanced RAG patterns
Improved performance and efficiency of knowledge retrieval systems in various applications.
Increased adoption of optimized GraphRAG and Agentic RAG solutions across industries requiring complex information synthesis.
Further commoditization of basic RAG while specialized, context-aware RAG systems become a competitive differentiator.
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