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The emergence of generative Large Language Models (LLM) brings new research opportunities for social scientists and demographers. In this tutorial, we will be introducing the following four areas of LLM:

• What is a generative LLM?
• The use of generative LLMs for the social sciences: overview of current research applications;
• A Python (langchain) tutorial on using a generative LLM, GPT-3.5-turbo, for building customized measurements out of political speech;
• Advanced topic: Embeddings, Vector Stores, and Retrieval Augmented Generation (RAG) in Python

Prerequisites	Proficiency in Python is not required, but a basic understanding of writing functions and loops will be helpful. Primary knowledge of Text as Data will also be beneficial. For a refresher, please check out the workshop materials on Text as Data.
Materials	https://github.com/jyang32/LLM_Workshop/tree/main