This research explores the feasibility of extracting information from Swedish PDF files and providing specific answers to user questions related to the Swedish Transport Administration (Trafikverket). It proposes an open-domain QA system using the Retrieval-Augmented Generation (RAG) architecture and compares the relevancy of two main Large Language Models (LLM): ChatGPT 3.5 and BERT. The implementation involves extracting text from Swedish PDF files, converting the text into manageable chunks, and leveraging large language models to deliver accurate answers. Additionally, a verification dataset comprising 23 questions, 23 generated answers from the solution, and 23 reference answers was created to verify the answers. The solution's accuracy, as indicated by the ROUGE score, was found to be 82% for the GPT model. However, the BERT model was less effective, providing general and lengthy answers instead of specific ones. Consequently, the BERT model was discarded after assessing the output. In conclusion, the study found that GPT 3.5 was a more effective LLM for answering questions related to the Swedish Transport Administration (Trafikverket) in Swedish. Furthermore, the GPT model demonstrated higher accuracy and relevancy in delivering specific answers.