This course provides a clear introduction to the principles and practice of prompt engineering. It aims to help learners understand how large language models interpret instructions and how careful prompting can improve the clarity, accuracy, and consistency of AI-generated output. We start by looking at the basics of generative AI and the factors that affect model behavior. Then, the course presents the Core Four framework: Instruction, Context, Persona, and Format. This framework is a practical way to design effective prompts for various tasks.
Learners will examine examples of weak and strong prompts, revise them through guided steps, and use techniques like Chain-of-Thought reasoning and few-shot prompting. The course also covers prompt templates, refining prompts, and applying prompts in real-world scenarios, such as coding help, text generation, summarization, and creative projects across multiple domains.
Throughout the course, short exercises and demonstrations show how slight changes in wording can greatly influence model output. By the end, learners will understand how to create purposeful prompts and use them effectively in academic, professional, and technical settings. This course provides a solid yet approachable foundation for anyone looking to work with AI more precisely and informed, offering practical skills that support confident, adaptable, and sustainable practice.
