Over the past few years, large language models (LLMs), like ChatGPT and Claude, have become instrumental in performing various natural language processing (NLP) tasks. They are proficient in natural language understanding and can produce quality responses to various NLP problems. Despite this, LLMs need to be guided and prompted in specific ways to have optimized and consistent outputs.

In this course, we will learn how to leverage LLMs to classify named entities in texts through zero-shot, single-shot, and few-shot classification. We will learn how to ensure consistent structure in our outputs via OpenAI’s new line of GPT-4o models and Pydantic. Most importantly, we will learn how to leverage these outputs to frame and answer humanities-specific qualitative and quantitative questions

This class runs every other day the week of  April 14th.

• Monday, 4/14/25
• Wednesday, 4/16/25
• Friday, 4/18/25

No materials or software are needed.

Register to attend online or access the recording asynchronously.

Check out our Research & Scholarship guides (including Constellate guide) for self-guided help. For questions, Ask Us.

Any person who requires a reasonable accommodation on the basis of a disability in order to participate in this program should contact Rafia Mirza at least one week prior to the event to arrange for the accommodation.