Prompt Engineering Fundamentals
Stop guessing at prompts. Learn the mechanics that make LLM outputs reliable, repeatable, and production-ready.
About
A practical, code-first course on getting dependable results from large language models. You will learn how LLMs actually process your words, then build a complete toolkit: prompt structure, few-shot examples, reasoning techniques, structured outputs, context engineering, and systematic evaluation. Every chapter includes real before-and-after prompts you can adapt to your own projects.
You will learn to
- ✓ Explain how tokenization, attention, and next-token prediction shape what a model does with your words
- ✓ Structure any prompt with the role, task, context, format pattern, and split it correctly across system and user messages
- ✓ Use few-shot examples and chain-of-thought techniques to raise output quality on hard tasks
- ✓ Get valid JSON and schema-conforming outputs that survive contact with production code
- ✓ Build small eval sets and iterate on prompts systematically instead of guessing
- ✓ Recognize and defend against hallucination, prompt injection, and long-conversation drift
How you'll learn
Read at your pace: diagrams, copy-paste prompts, worked steps.
Ready for the full build?
This course is free. When you want the production-grade version, start here.
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Curriculum
01Chapters12 lessons
- ¶1. How LLMs Actually Read Your Prompt9 min read
- ¶2. The Anatomy of a Good Prompt10 min read
- ¶3. System Prompts vs User Prompts9 min read
- ¶4. Few-Shot Examples Done Right10 min read
- ¶5. Chain-of-Thought and Reasoning Prompts10 min read
- ¶6. Structured Outputs: JSON, Schemas, and Contracts11 min read
- ¶7. Context Engineering: What to Include and What to Cut10 min read
- ¶8. Iterating and Evaluating Prompts Systematically11 min read
- ¶9. Failure Modes: Hallucination, Injection, and Drift11 min read
- ¶10. Prompting Coding Agents vs Chat Models10 min read
- ¶11. Wrap-Up: Your Prompt Engineering Playbook8 min read
- ¶12. Knowledge Check5 min