🔗 Agentic RAG vs long-context LLMs: a 171-question benchmark on 30 long PDFs

What happens when you evaluate agentic retrieval-augmented generation (RAG) against long-context LLMs? This article presents a rigorous benchmark of 171 questions asked across 30 long PDFs. The results highlight stark differences in performance between traditional RAG methods and long-context language models. The findings suggest that long-context LLMs excel at parsing complex documents, allowing for more nuanced responses. Data shows that while RAG can be effective, its reliance on external data sources may hinder performance on intricate queries. This comparison offers vital insights for researchers and practitioners considering which model to adopt for handling extensive textual information.