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Winter Registration Opens Nov 30
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TECH 16 — Large Language Models for Business with Python

Quarter: Winter
Instructor(s): Charlie Flanagan, Dima Timofeev
Duration: 2 days
Location: On-campus
Date(s): Feb 6—Feb 7
Class Recording Available: No
Class Meeting Day: Saturday and Sunday
Grade Restriction: No letter grade
Class Meeting Time: 9:30 am—3:30 pm (PT)
Tuition: $560
   
Refund Deadline: Jan 30
 
Unit(s): 1
   
Enrollment Limit: 60
  
Status: Registration opens Nov 30, 8:30 am (PT)
 
Quarter: Winter
Day: Saturday and Sunday
Duration: 2 days
Time: 9:30 am—3:30 pm (PT)
Date(s): Feb 6—Feb 7
Unit(s): 1
Location: On-campus
 
Tuition: $560
 
Refund Deadline: Jan 30
 
Instructor(s): Charlie Flanagan, Dima Timofeev
 
Grade Restriction: No letter grade
 
Enrollment Limit: 60
 
Recording Available: No
 
Status: Registration opens Nov 30, 8:30 am (PT)
 
 
Large language and multimodal models are transforming content creation, coding, and productivity. This course provides a practical introduction to applying large language models (LLMs) to real business and technical problems. Through an examination of OpenAI, Claude, Gemini, and related models, students learn how to select and use these tools to create competitive business advantage. Students gain hands-on experience building real-world applications using Python, LlamaIndex, and Hugging Face, including systems for text generation, translation, and sentiment analysis. Topics include:

  • Selecting appropriate model architectures for different use cases
  • Designing and building retrieval-augmented generation (RAG) systems from scratch
  • Understanding when prompt engineering is sufficient versus when fine-tuning is warranted
  • An introduction to agentic workflows and autonomous agents
  • An overview of the Model Context Protocol (MCP) and its application in real-world systems

The course features interactive coding sessions throughout.

Students are expected to have a basic understanding of Python and machine learning. Prior exposure to natural language processing is helpful but not required. This course relies on the use of an external, third-party tool that is not managed or supported by Stanford. Students must purchase their own tool subscriptions and can expect to spend $5–$20. Please see the course syllabus for more details.

CHARLIE FLANAGAN
Chief AI Officer, Balyasny Asset Management

Charlie Flanagan is the chief AI officer at Balyasny Asset Management, a large, multi-strategy hedge fund. Earlier, he worked for Google, where he was the data science lead for Google Duplex. He received an MS in software engineering from Harvard and an MBA from Columbia.

DIMA TIMOFEEV
Research Engineer, Balyasny Asset Management

Dima Timofeev has over a decade of experience in software engineering, AI/ML, autonomous systems, distributed systems, and large-scale data processing. He was a research engineer at 1X, which develops humanoid robots, and worked on self-driving cars at Waymo and Cruise. Before transitioning to embodied AI and later to finance, Timofeev spent five years at Google. He received an MS in computer science and computer engineering from Peter the Great St. Petersburg Polytechnic University, Russia.