Welcome to the Journey

Once, building technology meant writing strict rules and lines of precise instructions for computers to follow. But the rules have changed. Today, we do more than program machines. We guide intelligent systems that learn, reason, and create alongside us. What caused this shift?

The launch of ChatGPT demonstrated to the world that interacting with machines could feel genuinely human. Overnight, the public’s perception of artificial intelligence shifted from an abstract research topic to a tool that anyone could use. It transformed how people build software forever.

That was only the start. After ChatGPT, OpenAI (the company behind it) led from the front and released a wave of advanced tools that reshaped how developers build. What began as a conversational interface evolved into a comprehensive developer toolkit: the OpenAI SDK, which makes building intelligent applications accessible to any programmer.

All this potential is now in our hands. If you can harness the OpenAI SDK, you can build applications that think, generate, and adapt; the kind of tools that were once present in the domain of science fiction only. The SDK has transformed AI from an experimental to a practical tool, making it accessible to every developer.

Why learn the OpenAI SDK?

A key reason to learn how the OpenAI SDK works is that, unlike most software development kits (SDKs) tied to a single vendor or model family, it is designed for interoperability. It works well with other models. Whether you want to use models from OpenAI, Anthropic, Gemini, or future contenders, the same core SDK can handle them all.

Change the base URL to that provider’s API endpoint, swap in the new API key, update the model name, and you are ready to go. This design makes the OpenAI SDK a bridge, not a gatekeeper. It hides the infrastructure, allowing developers to focus on what matters: crafting intelligent systems, not managing endpoints.

Beyond its flexibility, the SDK is built around the concept of agentic AI: systems that not only respond but also act within defined boundaries. Here is what makes it stand out.

• Simplified agent development: The SDK offers lightweight abstractions for building AI agents that can hold goals, follow instructions, and use tools.

• Production-ready components: Core building blocks such as agents, handoffs, guardrails, and sessions let you construct scalable, reliable multi-agent systems that are as robust as any modern back-end service.

• Python-optimized integration: Built for developers who already use Python, it turns AI engineering into a natural extension of software engineering.

• Tracing, debugging, and evaluation: Visualization tools let you watch how your agents think. You can debug, trace, and fine-tune their decision-making in real time.

• Structured output and tool use: Agents can intelligently invoke custom functions, returning structured results that connect natural language to real-world business logic.

• Rapid development: Prebuilt patterns and components drastically reduce development time, letting you move from concept to working AI applications in days, not weeks.

In this course, we will move from understanding how the SDK works to building our own agents. The same tools that power ChatGPT are now under your control.

What are the prerequisites for this course?

You do not need a PhD in machine learning or a decade of coding experience to follow along. This course is designed for builders and learners interested in learning how to implement AI systems in practice.

You will get the most out of it if you have the following:

• A general understanding of how large language models (LLMs) work: You do not need to know the math behind transformers, but you should know that LLMs predict based on context and can be guided with prompts, examples, and tools.

• Basic Python skills: If you can write simple functions, work with packages, and read example code, you are ready.

• A rough idea of how application programming interfaces (APIs) work: Just the basics, such as sending a request, getting a response, and using an API key to authenticate. If you have ever used requests.get() to call an API, you are ready.

What will we do?

By the end of this course, you will know how to move from using AI to building with AI. We will go beyond single API calls and explore how to create full, intelligent applications powered by OpenAI’s tools.

You will learn how to:

• Understand how OpenAI’s ecosystem fits together, from simple text generation to complex agentic systems.

• Build and configure AI agents using the OpenAI SDK, connecting them to real-world tasks and data.

• Use APIs effectively by making calls, parsing responses, handling errors, and chaining operations together.

• Design structured, reliable workflows where natural language meets traditional programming logic.

Along the way, we will explore OpenAI’s core technologies that make this possible.

• GPT models: The large language models that power reasoning, conversation, and creative generation.

• Completions and embeddings APIs: The foundational interfaces for generating text and representing meaning mathematically, useful for search, clustering, and personalization.

• Responses API: A unified endpoint for conversational and multimodal interactions that simplifies how you connect models to user interfaces.

• Agents SDK: A framework for building agentic systems.

• AgentKit: A growing collection of ready-made tools and utilities that extend what your agents can do, from web browsing to data retrieval.

By the time you finish, you will not only understand what these technologies are, but you will also know how to make them work together. The goal is to give you a developer’s intuition for AI, that is, the ability to imagine something intelligent and then build it.