This course walks you through every aspect of prompt engineering: prompt anatomy, zero-shot and few-shot techniques, Chain of Thought, content generation and analysis, security, and evaluation. Each chapter includes hands-on exercises and knowledge-check questions.
You'll learn two fundamental techniques: zero-shot (no examples) and few-shot (with examples). You'll understand when each works best and how to choose examples.
Chain of Thought (CoT) is a technique that dramatically improves the reasoning ability of LLMs. You'll learn when and how to use CoT and its advanced variants.
You will learn techniques that go beyond the basics: self-refinement, metacognitive prompting, generated knowledge and other methods that significantly improve output quality.
Learn how to use LLMs effectively to write, debug, refactor and document code. You will master techniques specific to software-engineering tasks: code generation, debugging with stack traces and logic bugs, safe refactoring with frozen APIs and green tests, and tool-specific prompting for Copilot, Cursor and Claude Code.
You'll learn to craft effective prompts for content: articles, emails, reports, presentations. You'll master techniques for keeping brand voice, business tone calibration, document analysis with RAG-style grounding, and multilingual production pipelines. Practical patterns for writers, marketers and analysts.
Eval sets, metrics, LLM-as-judge, A/B testing, regression and data-driven iteration.
Vision prompting, OCR and documents, audio, grounding with bounding boxes.
JSON Schema, strict mode, tool calling, grammars, retry strategies.
