  This paper presents our approach to the EHRSQL-2024 shared task, which aims
to develop a reliable Text-to-SQL system for electronic health records. We
propose two approaches that leverage large language models (LLMs) for prompting
and fine-tuning to generate EHRSQL queries. In both techniques, we concentrate
on bridging the gap between the real-world knowledge on which LLMs are trained
and the domain specific knowledge required for the task. The paper provides the
results of each approach individually, demonstrating that they achieve high
execution accuracy. Additionally, we show that an ensemble approach further
enhances generation reliability by reducing errors. This approach secured us
2nd place in the shared task competition. The methodologies outlined in this
paper are designed to be transferable to domain-specific Text-to-SQL problems
that emphasize both accuracy and reliability.
