Understanding how protein sequence encodes structure and function remains one of the central challenges in the life sciences. Yet most protein language models still treat each sequence as an isolated datapoint. This forces the entire burden of evolutionary context into model parameters, which leads to blind spots in underrepresented families and amplifies the biases of sequence databases. Profluent’s new E1 family demonstrates that this constraint is no longer necessary. Retrieval augmentation, a technique that transformed natural language processing, is now beginning to reshape protein modeling by allowing models to incorporate evolutionary information at the moment of inference rather than storing it all in weights.