ChatGPT Prompt Engineering for Developers

Is this course completely free?

Yes, this course is completely free during DeepLearning.AI’s learning platform beta. You get full access to all 9 video lessons, 7 interactive code examples in Jupyter notebook environments, and hands-on practice with the OpenAI API without any cost. There are no hidden fees, subscriptions, or certificate charges. This is part of DeepLearning.AI’s mission to make cutting-edge AI education accessible to everyone. You simply need to create a free account on DeepLearning.AI’s platform to start learning immediately, with no credit card required.

What programming background do I need?

You only need a basic understanding of Python to take this course. If you’re comfortable with fundamental Python concepts like variables, functions, and basic data structures, you’re well-prepared. The course is beginner-friendly and doesn’t require deep programming expertise or prior machine learning knowledge. However, it’s also suitable for advanced machine learning engineers wanting to learn the latest prompt engineering techniques for LLMs. You don’t need experience with the OpenAI API—the course teaches you how to use it through hands-on examples in the provided Jupyter notebook environment.

What will I actually build during this course?

You’ll gain hands-on experience building several practical applications using the OpenAI API. Through interactive Jupyter notebook exercises, you’ll create systems that summarize lengthy texts like customer reviews, infer sentiment and extract topics from documents, transform text through translation and grammar correction, expand brief notes into full emails or documents, and build a custom chatbot that can have multi-turn conversations. Each lesson includes code examples you can modify and experiment with directly, allowing you to see how different prompts produce different results and helping you develop intuition for effective prompt engineering.

How long does it take to complete this course?

The course contains approximately 90 minutes (1.5 hours) of video content across 9 lessons. However, because the course emphasizes hands-on practice with interactive code examples, most learners spend 2-4 hours total including time experimenting with prompts in the Jupyter notebooks. You can complete the entire course in one sitting if you’re motivated, or spread it over a few days. The compact format makes it perfect for busy developers who want to quickly gain practical LLM skills without a major time investment. The self-paced structure lets you move at your own speed.

What are the two key principles for effective prompts mentioned?

While the course teaches these principles in detail through examples, they generally involve writing clear and specific instructions, and giving the model time to think. The first principle emphasizes being explicit about what you want—providing detailed context, specifying the desired output format, and using delimiters to clearly separate different parts of your input. The second principle recognizes that LLMs perform better when you structure prompts to guide them through step-by-step reasoning rather than asking for immediate answers to complex questions. The course demonstrates how to apply these principles systematically across different tasks, making your prompts more reliable and effective.

How does this differ from casual ChatGPT usage?

This course focuses on programmatically using LLMs through the OpenAI API to build applications, not just chatting with ChatGPT through the web interface. You’ll learn to integrate LLM capabilities into your own software, automate tasks at scale, and create custom solutions tailored to specific use cases. The course teaches systematic prompt engineering for developers building products, including how to iterate on prompts programmatically, handle edge cases, and create reliable applications. While casual ChatGPT users craft prompts manually for one-off tasks, this course teaches you to engineer prompts that work consistently across thousands of inputs in production applications.

Who are the instructors and what’s their expertise?

The course is co-taught by Isa Fulford, a member of the technical staff at OpenAI who works directly on ChatGPT and the API, and Andrew Ng, founder of DeepLearning.AI and one of the world’s leading AI educators. Isa brings insider knowledge of how these models actually work and what techniques the OpenAI team has found most effective. Andrew brings decades of experience making complex AI concepts accessible and practical for learners worldwide. This partnership between OpenAI and DeepLearning.AI ensures you’re learning the latest best practices directly from the people building and studying these technologies, not outdated or secondhand information. The course reflects cutting-edge understanding of how to effectively use the newest LLM models.