Prompt Engineering & Programming with OpenAI
Learn to design effective prompts, code with the OpenAI API, and build real-world LLM features that enhance workflows, automate tasks, and unlock product innovation.
Modules/Weeks
Weekly Effort
Discipline
School
Format
Cost
Course Description
Understanding how to work with large language models (LLMs) is becoming essential, as natural‑language interfaces are rapidly integrating into everyday products—from search engines to office software. Professionals who can craft clear prompts, navigate model limitations, and safely embed AI into workflows are in high demand. Mastering these skills not only future‑proofs your career but also gives your organization a competitive edge by accelerating development cycles and enabling new kinds of user experiences.
This course begins with the fundamentals of prompt engineering—the art of guiding an LLM’s output through well‑structured instructions. You’ll then move into hands‑on coding with the OpenAI API, learning to build and integrate features like text generation, document summarization, and image creation into real‑world applications. By the end, you’ll have completed several mini-projects that can serve as templates for your own work.
Designed for intermediate learners with a working knowledge of programming fundamentals—variables, functions, and JSON handling—this course is ideal for professionals in AI-adjacent roles, such as data scientists, ML engineers, and product managers, looking to gain hands-on experience with prompt engineering and API integration. It also welcomes tech‑savvy individuals eager to leverage LLMs programmatically to automate tasks, enhance products, or prototype new user experiences.
Course Prerequisites
- Basic Python programming knowledge
- Coding Environment: Google Colab
- OpenAI API
- DeepSeek API
What You Will Learn
By the end of this course, learners will be able to:
- Create Effective Prompts for LLMs: Use zero-shot, one-shot, and few-shot prompts to guide generative models toward specific outputs
- Implement LLM-Based Solutions: Connect to the OpenAI API and integrate generative AI features into notebooks or apps
- Generate Text, Images, and More: Leverage Python libraries (e.g., LangChain, OpenAI) to build advanced workflows
- Module 1: Prompt Engineering
By the end of this module, learners will be able to:
- Understand the basics of large language models (LLMs)
- Explain how tokens are generated from a prompt.
- Understand the difference between zero-shot, one-shot, and few-shot prompts
- Use prompts to generate email, summaries, and content
- Use prompts to interpret and generate images
- Generate code and SQL queries using prompts
- Read data from a file and draw graphs and charts using prompts
- Apply machine learning techniques and get results using prompts and their own datasets
- Explore the use of ChatGPT to help handle business tasks (generating marketing plans, logos, etc.)
- Reasoning LLM and Chain of thought prompting
- Module 2: Programming with the OpenAI API
By the end of this module, learners will be able to:
- Utilize OpenAI’s API for LLM-based programming
- Implement generative AI models in notebooks
- Use python libraries like LangChain and LLama-Index to analyze data using LLMs
- Generate images and audio using OpenAI’s API.Accordion content
Instructors
Johar received an M.A. in Economics from the Birla Institute of Technology and Science and is a Fellow of the Indian Institute of Management Calcutta. He received a Ph.D. in Information Systems from the Stern School of Business, New York University in 1994. Prior to joining Columbia, Johar has worked as a quantitative trader at Morgan Stanley, Credit Suisse and Deutsche Bank, at a tech startup (MSpoke), and has taught at NYU Stern School of Business and the Gabelli School of Business Fordham University.