arXiv:2605.30029v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems expose numerous design choices spanning query rewriting, chunking, retrieval depth, reranking, and context compression. In practice, these choices are often configured through heuristics, hindering systematic evaluation and reproducibility across settings. We argue that this challenge is best formulated as RAG architecture search. To support controlled and reproducible study of this problem, we introduce the RAG Intelligence Search Engine (RAISE), a comprehensive framework and benchmark for RAG hyperpa
The proliferation of RAG systems highlights the current ad-hoc design limitations, making systematic optimization and evaluation increasingly critical for further progress.
This development introduces scientific rigor to RAG design, moving from heuristic-based configurations to architectural search, which is essential for scaling AI systems and ensuring reliable outputs.
RAG system development shifts from artisanal tuning to a more automated and systematic architecture search approach, leading to more robust and performant AI applications.
- · AI researchers and developers
- · Enterprises deploying RAG
- · AI platform providers
- · Manual RAG tuners
- · Companies with suboptimal RAG deployments
Systematic optimization of Retrieval-Augmented Generation (RAG) system performance becomes standard practice.
Improved RAG performance leads to more reliable and trustworthy AI applications, accelerating AI adoption in critical sectors.
The complexity shift from human-in-the-loop tuning to automated search methodologies could free up significant engineering resources for more advanced AI problems.
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Read at arXiv cs.AI