s3: The new RAG framework that trains search agents with minimal data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Researchers at University of Illinois Urbana-Champaign have introduced s3, an open-source framework designed to build retrieval-augmented generation (RAG) systems more efficiently than current methods. s3 can benefit developers creating real-world large language model (LLM) applications, as it simplifies and reduces the cost of creating retriever models within RAG architectures. RAG retrieval The effectiveness of any RAG system hinges on the quality of its retrieval component. In their paper, the researchers categorize the evolution of RAG approaches into three distinct phases. “Classic RAG” systems rely on static retrieval methods with fixed queries, where retrieval quality is disconnected from the ultimate generation performance. These architectures struggle with queries requiring contextual or multi-hop reasoning. A subsequent phase, dubbed “Pre-RL-Zero ... Read full article.