Prompt Engineering: Frameworks and Techniques for LLMs

Large language models are incredibly powerful, but getting them to produce consistent, high-quality results requires more than just basic typing. Understanding how to structure your instructions systematically is the key to unlocking their true potential. This text-based course guides you from the absolute basics of natural language interaction to structured prompt engineering methodologies. You will learn how to design, test, and refine prompts that guide AI models with precision, ensuring reliable outputs for any task. What you'll learn: - Understand foundational prompt structures, including role prompting and few-shot learning. - Apply advanced reasoning frameworks like Chain-of-Thought and Step-Back prompting to solve complex problems. - Configure LLM parameters such as temperature and top-p to control creativity and determinism. - Design structured outputs using JSON mode and schema-based prompting for seamless integration. - Evaluate and test prompt performance systematically using modern evaluation concepts to ensure reliable results. The course begins with core definitions and basic model mechanics before moving into step-by-step framework implementations and practical evaluation strategies. This course is designed for beginners, developers, and product managers. No programming experience or prior background in artificial intelligence is required. Start reading today to build a structured foundation in prompt engineering.