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Prospective Students

Course Staff

Instructor (Guest) Co-instructor
Dawn Song Xinyun Chen
Professor, UC Berkeley Research Scientist, Google DeepMind


Reader: Tara Pande

Class Time and Location

Lecture: 3-5pm PT Monday at Latimer 120

Course Description

Large language models (LLMs) have revolutionized a wide range of domains. In particular, LLMs have been developed as agents to interact with the world and handle various tasks. With the continuous advancement of LLM techniques, LLM agents are set to be the upcoming breakthrough in AI, and they are going to transform the future of our daily life with the support of intelligent task automation and personalization. In this course, we will first discuss fundamental concepts that are essential for LLM agents, including the foundation of LLMs, essential LLM abilities required for task automation, as well as infrastructures for agent development. We will also cover representative agent applications, including code generation, robotics, web automation, medical applications, and scientific discovery. Meanwhile, we will discuss limitations and potential risks of current LLM agents, and share insights into directions for further improvement. Specifically, this course will include the following topics:

Enrollment and Grading (Subject to Change)

Please fill out the signup form if you are on the waitlist or can’t get added to the waitlist.

This is a variable-unit course. All enrolled students are expected to participate in lectures in person. Students enrolling in one unit are expected to submit an article that summarizes one of the lectures. Students enrolling in more than one unit are expected to submit a lab assignment and a project instead of the article. The project of students enrolling in 2 units should have a written report, which can be a survey in a certain area related to LLMs. The project of students enrolling in 3 units should also have an implementation (coding) component that programmatically interacts with LLMs, and the project of students enrolling in 4 units should have a very significant implementation component with the potential for either real world impacts or intellectual contributions. The grade breakdowns for students enrolled in different units are the following:

  1 unit 2 units 3/4 units
Participation 50% 20% 10%
Article 50%    
Lab   20% 10%
  Proposal   10% 10%
  Milestone   10% 10%
  Presentation   25% 25%
  Report   15% 15%
  Implementation     20%

Lab and Project Timeline

  Released Due
Project group formation TBA TBA
Project proposal TBA TBA
Project milestone TBA TBA
Project presentation TBA TBA
Project final report TBA TBA

Office Hours