Calling an LLM API takes minutes. Making it reliable, secure, and affordable takes design. In this course, you will learn how to integrate model APIs with Python and build prompt-driven AI features that hold up in production.

You’ll learn how the API request and response cycle works, how to choose between raw HTTP and an SDK, and how to select a model by weighing capability, latency, cost, and risk. You’ll explore how message roles, generation parameters, and conversation history shape chat interactions, and how to handle errors with bounded retries while protecting API keys and controlling costs. The course also covers reusable prompt templates, structured outputs validated with Pydantic, and retrieval-augmented generation that grounds answers in private or current information. What makes this course unique is its focus on engineering judgment over tool tutorials. Not just calling the API, but making it dependable. By the end, you will be able to build model-powered features that are predictable, secure, and cost-aware.