Although retrieval augmented generation (RAG) has proven to be an effective approach for creating question-answering systems on a corpus of documents, there is a need to improve the performance of these systems, especially in the regulatory domain where clear and accurate answers are required. This paper outlines the methodology used in our submission to the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task at the Regulatory Natural Language Processing Workshop (RegNLP 2025). The goal is to improve document retrieval (Shared Task 1) and answer generation (Shared Task 2). Our pipeline is constructed as a two-step process for Shared Task 1. In the first step, we utilize a text-embedding-ada-002-based retriever, followed by a RankGPT-based re-ranker. The ranked results of Task 1 are then used to generate responses to user queries in Shared Task 2 through a prompt-based approach using GPT-4o. For Shared Task 1, we achieved a recall rate of 75%, and with the prompts we developed, we were able to generate coherent answers for Shared Task 2.