Academic advising is essential to student success, encompassing a wide range of inquiries from general program information to more personalized concerns. To streamline the process and limit human interaction in this work, we introduce an innovative AI-driven chatbot that harnesses the power of large language models (LLMs), specifically LLaMA, alongside retrieval-augmented generation (RAG) technique. This approach aims to automate and enhance the student advising process, offering more efficient and accurate support for students. Unlike conventional chatbots constrained by rigid, intent-based frameworks, our system dynamically retrieves and synthesizes information from continuously updated knowledge bases. This approach enables the chatbot to provide accurate, context-aware, and reliable responses to student inquiries. In comparison to existing systems, the proposed solution effectively mitigates common issues such as hallucinations and outdated information, delivering factually grounded and scalable performance. This paper presents an overview of the development lifecycle, encompassing dataset creation, system architecture, and experimental validation. Our experiments demonstrate that Retrieval-Augmented Generation (RAG) excels in accuracy, scalability, and relevance, making it a highly effective solution for academic advising chatbots. RAG improves the handling of queries, automates routine tasks, and reduces advisor workload, allowing them to focus on complex cases. This solution enhances student support by balancing efficient automation with personalized guidance.