Course Overview
Learn about the course’s structure, intended audience, and the key learning objectives set for your journey as a prompt engineer.
We'll cover the following...
Welcome to this comprehensive course on professional prompt engineering. In recent years, large language models (LLMs) have demonstrated incredible capabilities in understanding and generating human language. However, their true potential is unlocked only when we can move beyond simple questions and answers to reliably direct their power. This requires a systematic, engineering-driven approach.
This course introduces the core concepts and practical techniques of prompt engineering. Prompt engineering focuses on designing and optimizing prompts so that a generative model produces accurate, consistent, and safe outputs. The course covers foundational concepts through to production-ready techniques used to build reliable AI systems.
Why take this course?
As generative AI becomes more integrated into professional applications, the ability to simply get an answer from an AI is no longer enough. The demand is for engineers who can consistently deliver the right answer in the right format, while ensuring the system is safe, fair, and reliable. This course is designed to elevate your skills from a casual prompter to a professional prompt engineer.
We will not focus on simple tricks or hacks. Instead, we will build a deep, practical understanding of the principles that underpin effective AI interaction. By the end of this course, you will have a systematic framework for analyzing problems, designing robust prompts, and managing them throughout a professional software life cycle. This is the key to building AI applications that are not just powerful, but also predictable and trustworthy.
Intended audience
This course is specifically tailored for experienced technology professionals who are already working with or building generative AI systems. It is designed to formalize and deepen your expertise in prompt engineering. Our ideal learners include:
Generative AI engineers who want to move beyond prototyping and master the skills needed for production-grade reliability and safety.
Software engineers who are integrating LLM capabilities into new or existing applications and need a systematic framework for managing prompts as a core part of their codebase.
AI/ML engineers looking to specialize in the applied side of LLMs, focusing on the critical interface between the model and the user’s intent.
Technical architects who need to understand the principles of building robust, scalable, and secure AI systems that rely on effective prompting.
Prerequisites
To get the most out of this course, we expect you to have a solid foundation in a few key areas:
Core generative AI concepts: You should have a basic understanding of what an LLM is, how it works at a high level, and concepts like tokens and context windows.
Basic API concepts: A general understanding of what an API is and the conceptual flow of a client-server request is necessary to understand how tools are implemented.
Course structure
We have logically structured this course to take you on a journey from foundational principles to advanced, production-level operations. Here is a brief look at what we will learn in each chapter:
Chapter 1 (Introduction): We will define prompt engineering and outline how it has progressed from an ad hoc practice to a formal engineering discipline.
Chapter 2 (Fundamentals of Prompt Engineering): We will learn the core components of effective prompts, including how to define clear objectives and manage ambiguity.
Chapter 3 (Instruction Design): We will dive deep into the art of instruction, learning to control output format, style, and advanced reasoning.
Chapter 4 (Context and Grounding): We will cover techniques for grounding model outputs in verifiable data and mitigating prompt-injection attacks.
Chapter 5 (Tools and Structured Actions): We will learn how to give AI the ability to act by mastering the prompt engineering required for tool use.
Chapter 6 (Production and Operations): Finally, we will cover how to manage prompts throughout a professional software life cycle with automated testing and evaluation.