OpenAI Prompt Engineering for Improved Performance

What you'll learn

Crafting prompts that consistently deliver high-quality, relevant, and efficient results is a critical skill for today’s software developers working with large language models. Even experienced developers create prompts that work well but still leave performance, reliability, or clarity on the table. In this course, OpenAI Prompt Engineering for Improved Performance, you’ll learn to design and refine prompts that maximize accuracy, control output format, and optimize model response time in real-world developer workflows. First, you’ll explore advanced techniques for prompt engineering, including role definition, explicit constraints, and output structure that produce deterministic, production-ready results. Next, you’ll discover how to evaluate and apply strategies for reducing token load, improving query efficiency, and selecting the correct model parameters for speed and reliability. Finally, you’ll learn how to leverage system messages and few-shot prompting to steer model behavior, maintain context across multi-turn interactions, and guide complex reasoning with structured examples. When you’re finished with this course, you’ll have the skills and knowledge of advanced prompt engineering needed to reliably produce precise, efficient, and maintainable LLM-powered solutions for software development.