Google DeepMind Finds a Fundamental Bug in RAG: Embedding Limits Break Retrieval at Scale – MarkTechPost

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.

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