Building Systems with the ChatGPT API

Building production-ready applications with large language models requires more than just writing simple prompts. To create robust, automated workflows, you must learn how to treat language models as components of a larger, interconnected system. This text-based course guides you through the foundational concepts of structured LLM integration, teaching you how to build reliable, multi-step applications that handle real-world complexity. By completing this course, you will transform from a basic prompt writer into a system designer capable of structuring complex inputs, handling API responses programmatically, and ensuring the safety of your AI-driven workflows. What you'll learn: - Understand foundational API concepts, token limits, and structured response formats - Chain multiple API calls together to process complex, multi-step tasks systematically - Apply input validation and safety classification techniques to filter inappropriate content - Design robust evaluation strategies to test system outputs for quality and accuracy - Integrate modern prompt engineering patterns such as chain-of-thought processing inside your code - Configure error handling and fallback mechanisms to ensure system reliability We begin with core definitions and the mechanics of API communication, then transition into structuring multi-stage workflows, and finally explore evaluation and safety guardrails. This course is designed for software developers, product builders, and technology enthusiasts who want to move beyond the chat interface. No prior experience with artificial intelligence APIs is required, though a basic understanding of programming concepts will help you get the most out of the written examples. Start building reliable, automated systems today.