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Updated: Dec 22, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Retrieval Augmented Generation: What Works and Lessons Learned
Peter L Elkin1,2,3, Guresh Mehta1, Frank LeHouillier1
1Department of Biomedical Informatics, University at Buffalo.
Abstract:
Retrieval Augmented Generation has been shown to improve the output of large language models (LLMs) by providing context to the question or scenario posed to the model. We have tried a series of experiments to understand how best to improve the performance of the native models. We present the results of each of several experiments. These can serve as lessons learned for scientists looking to improve the performance of large language models for medical question answering tasks.
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