AI RESEARCH
AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines
arXiv CS.LG
•
ArXi:2605.02967v1 Announce Type: new Retrieval-Augmented Generation (RAG) enhances LLMs, but performance is highly sensitive to complex architecture designs and hyper-parameter configurations, which currently rely on inefficient manual tuning. We present AutoRAGTuner, a declarative, configuration-driven framework that automates the RAG life cycle: construction, execution,evaluation, and optimization. AutoRAGTuner employs a modular architecture to decouple pipeline stages through a component registration mechanism. To unify heterogeneous data, we.