Modern Prompt Engineering: Structuring AI Workflows

As artificial intelligence transitions from basic text completion to sophisticated agentic systems, the way we communicate with models must also evolve. Moving beyond simple trial-and-error prompting is essential for building reliable, production-ready AI applications. This written course guides you through the shift from casual prompting to systematic prompt engineering. You will learn how to design robust instructions, implement modern retrieval-augmented generation patterns, and establish evaluation frameworks to ensure consistent model performance. What you'll learn: - Understand the foundational concepts of Large Language Models and how prompt engineering has matured into a structured discipline. - Apply advanced prompting techniques, including few-shot prompting, chain-of-thought reasoning, and system-level instructions. - Structure model outputs using JSON schemas to ensure reliable integration with external software applications. - Explore retrieval-augmented generation concepts and how semantic search enhances prompt context. - Implement basic prompt evaluation strategies to measure consistency, accuracy, and mitigate model drift. - Identify security risks such as prompt injection and learn modern defense patterns to protect your AI workflows. The course begins with essential terminology and the mechanics of language models, then moves systematically through advanced design patterns, structured outputs, and modern evaluation workflows. You will read clear explanations, analyze real-world code snippets, and complete written exercises to solidify your understanding. This course is designed for beginners, developers, and aspiring AI practitioners who want to build a solid foundation in modern prompt engineering. No prior programming experience or machine learning background is required. Start reading today to transition from basic text prompts to professional AI workflow design.