Antibodies are successful biotherapeutics used for the treatment of various diseases. Throughout their therapeutic development, antibody candidates require optimization for drug developability, while retaining their functionality. This task remains a significant challenge as it is constrained by low-throughput experimental measurements. Retrieval Augmented Generation (RAG) was developed in natural language processing to generate more accurate text responses combining a retriever, a generator and a knowledge database. Here, we present a novel adaptation of this framework for the developability optimization of antibodies. Using solubility as a proof-of-concept, we demonstrate that this framework generates optimized antibody sequences with improved solubility scores, when evaluated in silico. This RAG framework allows precise control over the optimization process with the aim of preserving functionality of the antibody candidate. Moreover, the modular design enables adaptability across diverse optimization campaigns using a generalizable knowledge database, which has the potential to substantially reduce experimental efforts required for antibody developability optimization.