Designing System Prompts and Evaluators for AI Agents

As AI applications evolve from simple chatbots to autonomous agents, the ability to write precise, structured system prompts becomes a critical skill. Understanding how to guide an AI's behavior and systematically evaluate its outputs is the foundation of building reliable, production-ready systems. This text-based course guides you through the fundamental principles of prompt engineering for AI agents. You will progress from writing basic instructions to designing complex system prompts, structuring schemas for evaluators, and implementing automated feedback loops. By reading through clear explanations and studying practical code examples, you will gain the confidence to program predictable and robust AI behaviors. What you'll learn: Understand the core principles of system prompting and agent architecture; Design structured schemas to enforce specific output formats and constraints; Build automated evaluator prompts to judge and score AI responses; Implement reasoning frameworks within prompts to improve agent decision-making; Apply modern SDK integration patterns to generate reliable, structured text; Practice refining prompts through systematic testing and iterative feedback. The course begins with essential definitions and the core mechanics of system prompts before moving into schema design, structured outputs, and evaluation patterns. You will explore written walkthroughs of real-world scenarios, learning how to connect prompts to code to build functional agent components. This course is designed for software developers, product builders, and tech enthusiasts who are new to AI engineering. No prior experience with AI agents or advanced machine learning is required, though a basic familiarity with programming concepts is helpful. Start reading today to master the foundations of system prompt engineering and begin building your own intelligent AI agents.