  The BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation aims to
reduce the administrative burden on clinicians by automating the creation of
critical sections of patient discharge letters. This paper presents our
approach using the Llama3 8B quantized model to generate the "Brief Hospital
Course" and "Discharge Instructions" sections. We employ a zero-shot method
combined with Retrieval-Augmented Generation (RAG) to produce concise,
contextually accurate summaries. Our contributions include the development of a
curated template-based approach to ensure reliability and consistency, as well
as the integration of RAG for word count prediction. We also describe several
unsuccessful experiments to provide insights into our pathway for the
competition. Our results demonstrate the effectiveness and efficiency of our
approach, achieving high scores across multiple evaluation metrics.
