David Mimno is an Associate Professor and Chair of the Department of Information Science in the Ann S. Bowers College of Computing and Information Science at Cornell University. He holds a Ph.D. from UMass Amherst and was previously the head programmer at the Perseus Project at Tufts as well as a researcher at Princeton University. Professor Mimno’s work has been supported by the Sloan Foundation, the NEH, and the NSF.
LLM Tools, Platforms, and PromptsCornell Course
LLM Tools, Platforms, and Prompts ()
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Course Overview
In this course, you will discover how to work directly with some of today's most powerful large language models (LLMs). You'll start by exploring online LLM-based systems and seeing how they handle tasks ranging from creative text generation to language translation. You'll compare how models from major organizations like OpenAI, Google, and Anthropic differ in their outputs and underlying philosophies.
You will then move beyond web interfaces to identify how to find and load various foundation models through the Hugging Face hub. By mastering Python scripts that retrieve and run these models locally, you'll gain deeper control over prompt engineering and understand how different model architectures respond to your requests. Finally, you'll tie all these skills together in hands-on projects where you generate text, analyze tokenization details, and assess outputs from multiple LLMs.
Key Course Takeaways
- Identify capabilities of online systems that are based on language models
- Compare relative outputs among commercially available LLMs and contrast capabilities and affordances
- Find publicly available models through the Hugging Face hub
- Describe key players in the LLM development community and compare their philosophies
- Implement Python scripts, via the Hugging Face Transformers API, to load and interact with large language models
- Craft effective prompts that generate meaningful text for answering questions or performing tasks
- Analyze and explain differences in model responses, drawing on prompt design and model-specific properties
How It Works
Course Length
2 weeks
Effort
6 to 8 hours of study per week
Format
100% online, instructor-led
Course Author
David Mimno
Associate Professor
Cornell Bowers Computing and Information Science
Associate Professor and Chair of the Department of Information Science, Cornell Bowers Computing and Information Science
Who Should Enroll
- Engineers
- Developers
- Analysts
- Data scientists
- AI engineers
- Entrepreneurs
- Data journalists
- Product managers
- Researchers
- Policymakers
- Legal professionals
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