Retrieval-augmented generation (RAG) has become a standard way to ground large language model outputs in external knowledge. But practitioners face a sprawling configuration space, with many possible choices for retrieval, generation, and integration. A new arXiv preprint introduces RAG-NIMBF, a framework intended to make this analysis more systematic.
According to the abstract, RAG-NIMBF is both non-invasive and modular, meaning it can likely be applied without disrupting existing pipelines and can be adapted to different components. The stated goal is automated RAG configuration analysis, which could help practitioners compare setups more efficiently.
The paper is announced as new on arXiv, but the abstract provides limited detail on the specific methods or results. As a single source, it offers a preliminary look at the framework's aims rather than a validated benchmark outcome.