The RAG Paradox: When Retrieved Information Conflicts with Model Knowledge
Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI systems that can access up-to-date information and cite sources. The promise is elegant: combine the reasoning capabilities of large language models with the freshness and specificity of retrieved documents. But there's a fundamental problem lurking beneath this