Prompt Engineering Foundations for Modern LLM Workflows

As artificial intelligence becomes central to modern software and business, knowing how to communicate effectively with Large Language Models (LLMs) is a critical skill. Standard conversational inputs often lead to unpredictable results, making structured prompt design essential for building reliable AI-powered solutions. This course guides you from the foundational mechanics of language models to advanced prompt engineering methodologies. You will learn how to transition from basic trial-and-error prompting to systematic prompt design, enabling you to generate consistent, structured, and highly accurate AI outputs for real-world applications. What you'll learn: - Understand the core mechanics of Large Language Models and how they process context. - Apply advanced prompting techniques such as Few-Shot Prompting and Chain-of-Thought reasoning. - Design structured prompts to guarantee outputs in reliable formats like JSON for API integration. - Implement prompt chaining and basic Retrieval-Augmented Generation (RAG) patterns to handle complex tasks. - Analyze and evaluate prompt performance systematically to reduce hallucinations and bias. - Configure system prompts and model parameters to control output creativity and structure. You will start by exploring the core principles of how LLMs interpret text before moving on to practical structural frameworks and advanced reasoning patterns. Through clear written explanations and step-by-step text and code examples, you will learn how to build robust prompt pipelines. This course is designed for developers, product managers, and tech enthusiasts who want to master AI interaction from the ground up. No prior experience with programming or AI is required, as we begin with fundamental concepts before advancing to practical implementations. Start building more predictable, powerful, and intelligent AI interactions today.