Imagine having an AI research assistant that can instantly sift through millions of scientific papers to answer your complex biomedical questions with pinpoint accuracy and provide direct links to the source material. That's the promise of BioRAGent, a new retrieval-augmented generation (RAG) system designed to transform how researchers and professionals access biomedical information. Traditional search engines often struggle with the nuances of scientific language, leading to irrelevant results and wasted time. BioRAGent tackles this challenge by using large language models (LLMs) not just to find answers but also to enhance the search process itself. It cleverly expands your initial query with relevant synonyms and related terms, ensuring a more comprehensive search. Then, it extracts the most pertinent snippets from the top articles and uses them to generate concise, evidence-based answers, complete with citations. This innovative approach combines the power of LLMs with the reliability of established scientific literature, minimizing the risk of hallucinations – a common issue with AI-generated content. BioRAGent’s user-friendly interface offers two answer formats: a concise paragraph and a version with inline citations linked directly to PubMed articles. This transparency allows users to verify the information and delve deeper into the source material. While BioRAGent has already shown promising results in the BioASQ 2024 challenge, its potential extends far beyond. Future developments aim to enhance the system’s interactive capabilities, allowing users to fine-tune prompts and evaluate different LLMs. This opens doors for personalized, precision searching in the biomedical field, accelerating research and discovery. BioRAGent represents a significant leap towards a future where AI empowers researchers and professionals with quick, accurate, and transparent access to the vast ocean of scientific knowledge.