  Pharmacovigilance (PV) is essential for drug safety, primarily focusing on
adverse event monitoring. Traditionally, accessing safety data required
database expertise, limiting broader use. This paper introduces a novel
application of Large Language Models (LLMs) to democratize database access for
non-technical users. Utilizing OpenAI's GPT-4, we developed a chatbot that
generates structured query language (SQL) queries from natural language,
bridging the gap between domain knowledge and technical requirements. The
proposed application aims for more inclusive and efficient data access,
enhancing decision making in drug safety. By providing LLMs with plain language
summaries of expert knowledge, our approach significantly improves query
accuracy over methods relying solely on database schemas. The application of
LLMs in this context not only optimizes PV data analysis, ensuring timely and
precise drug safety reporting -- a crucial component in adverse drug reaction
monitoring -- but also promotes safer pharmacological practices and informed
decision making across various data intensive fields.
