Real Estate with AI:An agent based on LangChain

Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions.</p

The future of learning?: The role of large language models in Danish high schools explored

This thesis examines the usage of Large Language Models (LLM) in Danish high schools (HEI) by students, focusing on the influence upon educational practices and learning. It uses a mixed-methods approach, combining qualitative interviews with teachers and students, and quantitative surveys from students to collect the main body of our data. The thesis employs the Forced Response Design (FRD) variant of the Randomized Response Technique (RRT) to ensure respondent anonymity and accuracy in sensitive quantitative data collection.The findings reveal two things in relation to LLM use in HEI education. On one hand, LLMs provide support in learning processes by offering assistance and better understanding of various academic subjects. On the other hand, challenges such as dependency on LLM use, potential biases in model outputs, and the need for proper rules and training for both teachers and students are highlighted. Likewise, it is revealed that gender has an influence as far as dependency on LLM use, whereas women have a larger tendency to depend upon it, whilst men are using it more frequently. Another aspect of the analysis is the exploration of how LLMs influence student engagement and have pedagogical impact on teaching methodologies. The findings provide a clarification of how LLMs can either complement or disrupt traditional educational practices. The analysis leads to the thesis identifying best practices and areas that require further attention to optimize the integration of LLMs in the classroom.The thesis concludes with recommendations for effectively integrating LLMs into educational settings. It suggests the development of learning about LLMs for teachers and students, implementation of rules to address proper LLM use, and ongoing assessment of LLM performance and impact on student learning. These steps aim to maximize the benefits of LLMs while mitigating potential drawbacks, ensuring their responsible and effective use in education