Building Systems with the ChatGPT API

Is this course completely free?

Yes, this course is completely free during DeepLearning.AI’s learning platform beta. You get full access to all 11 video lessons, 9 interactive code examples in Jupyter notebooks, and hands-on practice with the OpenAI API without any cost. There are no subscriptions, hidden fees, or charges for certificates. You simply need to create a free account on DeepLearning.AI’s learning platform to start immediately. This accessibility reflects DeepLearning.AI’s commitment to democratizing cutting-edge AI education for developers worldwide.

Do I need to take “ChatGPT Prompt Engineering for Developers” first?

No, it’s not a prerequisite, though the course builds on similar concepts. If you have basic understanding of how to use the ChatGPT API and write prompts, you can jump directly into this course. However, if you’re completely new to prompt engineering, taking “ChatGPT Prompt Engineering for Developers” first will provide helpful foundational knowledge. That said, the course is designed to be accessible to beginners with just basic Python skills, so you can start here if you’re eager to learn about building systems specifically. The instructors explain concepts clearly enough for newcomers while moving at a pace that keeps experienced developers engaged.

What programming skills do I need?

You only need basic Python programming skills to complete this course. Understanding variables, functions, loops, and basic data structures is sufficient. The course doesn’t require advanced programming knowledge, machine learning expertise, or prior experience with APIs. However, intermediate and advanced machine learning engineers will also benefit from learning cutting-edge prompt engineering techniques for building LLM systems. All code examples are provided in Jupyter notebooks where you can run, modify, and experiment with the code directly, making it easy to learn by doing.

What’s the difference between simple prompts and building systems?

Simple prompts involve single interactions with an LLM—you send one prompt and get one response. Building systems means creating complex workflows where multiple prompts chain together, each using outputs from previous steps. For example, a customer service chatbot system might first classify the user’s query type, then route to different specialized prompts based on that classification, check outputs for safety and accuracy, and finally generate a response. This course teaches you to orchestrate these multi-step processes using Python code that manages the flow, evaluates intermediate results, and makes decisions about what to do next, enabling you to build sophisticated applications that handle real-world complexity.

What will I build in the hands-on exercises?

You’ll build progressively more complex systems culminating in a complete customer service chatbot. Along the way, you’ll create systems that classify user queries to determine appropriate responses, implement content moderation to filter unsafe inputs or outputs, use chain-of-thought reasoning to break complex problems into steps, chain multiple prompts together where each step builds on previous completions, and evaluate LLM outputs for accuracy and relevance. Each lesson includes interactive Jupyter notebook exercises where you can experiment with the code and immediately see results. The final project integrates all techniques into a working chatbot that demonstrates production-ready patterns for LLM systems.

What are chain-of-thought reasoning and prompt chaining?

Chain-of-thought reasoning involves prompting the LLM to think step-by-step through a problem rather than jumping directly to an answer, which improves accuracy on complex tasks requiring multi-step reasoning. The course teaches you to structure prompts that guide the model through logical steps. Prompt chaining means breaking complex workflows into multiple prompts where each prompt handles one specific subtask and passes its output to the next step. For instance, rather than one massive prompt trying to do everything, you might chain prompts for extraction, validation, transformation, and generation. The course shows when and how to use each technique, and how to implement these patterns efficiently in Python code.

How does this course prepare me for production applications?

This course emphasizes practical patterns for building reliable, production-ready systems. You’ll learn to evaluate inputs for safety before processing, check outputs for quality and accuracy, handle edge cases and errors gracefully, and monitor system performance. The course teaches responsible AI practices including content moderation and safety checks. You’ll understand how to split complex tasks into manageable subtasks that are easier to debug and maintain. The customer service chatbot you build demonstrates real-world architecture patterns you can apply to your own applications. These skills bridge the gap between simple prompt experiments and robust systems that handle thousands of users in production environments.