Retrieval-Augmented Generation (RAG) systems generally rely on dense embedding models that map queries and documents into fixed-dimensional vector spaces. While this approach has become the default for many AI applications, a recent research from Google DeepMind team explains a fundamental architectural limitation that cannot be solved by larger models or better training alone. What Is […] The post Google DeepMind Finds a Fundamental Bug in RAG: Embedding Limits Break Retrieval at Scale appeared first on MarkTechPost.
Google DeepMind Finds a Fundamental Bug in RAG: Embedding Limits Break Retrieval at Scale
Retrieval-Augmented Generation (RAG) systems generally rely on dense embedding models that map queries and documents into fixed-dimensional vector spaces. While this approach has become the default for many AI applications, a recent research from Google DeepMind team explains a fundamental architectural limitation that cannot be solved by larger models or better training alone. What Is […] The post Google DeepMind Finds a Fundamental Bug in RAG: Embedding Limits Break Retrieval at Scale appeared first on MarkTechPost.