Alzheimer’s Disease (AD) presents profound challenges to healthcare systems worldwide, necessitating efficient access to accurate information for optimal care delivery. Effective management of AD requires timely access to accurate information spanning disease etiology, diagnosis, treatment options, and caregiving strategies. However, the vast and constantly evolving body of AD-related literature poses a considerable barrier to efficient information retrieval, particularly for healthcare professionals operating in time-constrained environments. This paper outlines the objectives of a specialized Retrieval-Augmented Generation (RAG) system designed for answering questions related to Alzheimer’s Disease (AD), employing prompt engineering and utilizing Pinecone as the vector database. With the goal of enhancing accessibility and comprehension of AD-related information, the system aims to efficiently retrieve relevant data from diverse sources and generate contextually relevant answers tailored to user queries. By leveraging advanced techniques in prompt engineering and vector similarity search, the RAG system empowers healthcare professionals, patients, and caregivers with timely access to accurate and comprehensive information, ultimately facilitating informed decision-making and improving patient outcomes in AD management.