Recently, information retrieval and synthesis approaches have undergone a significant transformation with the advancement of information technology, especially artificial intelligence. However, the application of artificial intelligence in supporting candidates in gathering companies’ information during recruitment processes remains limited. This research proposes a question-answering system using Retrieval Augmented Generation (RAG) techniques to support recruitment interviews, such as chatbots. Company-related data is collected from multiple sources (corporate websites, reports, forums) then processed and stored in a database as a unique knowledge base. After that, RAG models are applied to construct query and response systems that support candidates throughout the interview session. Within the scope of this study, data were aggregated from 20 different companies, and various RAG techniques were analysed in a comparative manner. The result shows that all RAG models could generate responses with an accuracy of up to 91%, indicating the potential for practical implementation and expansion across diverse domains.