This thesis explores the potential of large language models to create a cost-effective and practicalsupport chatbot for the real estate industry. The goal is to develop a solution that assists users byproviding relevant information tailored to industry standards and individual user data, deliveredthrough an engaging chat interface. The use of RAG (Retrieval-Augmented Generation) isinvestigated as a means to improve the accuracy and reliability of the chatbot by providingaccess to real-time updates and a diverse range of data sources, including legal documents,contracts, and publicly available online information. The effectiveness of the chatbot is evaluatedbased on response accuracy, latency, and cost-efficiency. The study makes use of metrics such asMean Average Precision, Normalized Discounted Cumulative Gain and Hit Rate to assess theperformance of the vector search solution used for RAG. We conclude that optimizationtechniques like RAG, system instruction- and prompt -engineering are vital to ensure highresponse quality, user experience and cost-efficiency.