Advanced Prompt Engineering: Patterns & Systems

About this course

You’ve moved past the "magic word" stage of prompting. You know how to get a decent response, but you’re hitting a wall when it comes to consistency, complex tasks, or integrating LLMs into larger software. This course is for the serious builder and thinker who wants to transform prompt engineering from an art into a robust, repeatable engineering discipline. We’re not chasing fleeting tricks; we're building foundational skills. Forget endlessly tweaking a single prompt. We'll dive deep into designing entire prompt *systems*. This means understanding how to break down complex problems, orchestrate multiple LLM calls, manage state, and integrate external tools seamlessly. You'll learn the patterns used by leading AI teams to achieve predictable, high-quality outputs even in challenging domains. We'll dissect cutting-edge techniques like Chain-of-Thought reasoning, persona-based prompting, few-shot learning, and self-correction mechanisms. You'll gain hands-on experience structuring prompts for specific API features, debugging responses, and evaluating performance systematically. This isn't about memorizing prompts; it's about developing the architectural mindset to build intelligent agents. By the end, you won't just be a better prompter; you'll be an architect of AI behaviour. You'll confidently approach any LLM-based problem, knowing how to design, implement, and iterate on powerful, reliable prompt solutions that scale.

Requirements

• Intermediate understanding of Large Language Models (LLMs).
• Experience with basic prompt construction (e.g., using ChatGPT or an LLM API).
• Familiarity with Python is recommended for hands-on exercises.