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Prompt Engineering Tutorial

Write prompts that give reliable, checkable answers, then test and ship them like code

Beginner4 lessonsabout 20 min of reading

This course is for students, developers and analysts who already use an AI assistant but get uneven results, and for anyone who has to put a prompt inside a real product. It assumes no machine-learning background. Where code appears it is short Python that runs without any account or API key.

By the end you will be able to rewrite a vague request into a prompt with a clear role, task, context, constraints and output format; choose and write few-shot examples; ask for output your program can parse and validate; split a hard job into steps; and build a small test set so you can tell whether a prompt change helped or hurt.

The lessons run from writing to engineering. You start with the anatomy of one good prompt, add examples and structured output, then learn to break big tasks apart and refine them, and finish with testing, prompt injection and the habits (versioning, regression sets) that make a prompt safe to ship. For the machinery underneath, see Large Language Models, and for grounding answers in your own documents see Retrieval-Augmented Generation.

Start lesson 1: Prompt Engineering BasicsRide it: tricks, quizzes, XP

Prompt Engineering lessons

  1. 1Prompt Engineering Basics: Anatomy of a Good PromptPrompt engineering basics: the five parts of a good prompt (role, task, context, constraints, output format) with a before and after rewrite you can copy.5 min read
  2. 2Few-Shot Prompting and Structured Output in Prompt EngineeringFew-shot prompting explained: how to choose examples, avoid label drift, and ask a model for JSON your code can validate, with a runnable Python checker.5 min read
  3. 3Prompt Engineering: Step-by-Step Reasoning and Iterative RefinementBreak hard tasks into prompt chains, ask for step-by-step reasoning when it helps, and refine a prompt methodically, using a refund-eligibility example.5 min read
  4. 4Prompt Engineering in Production: Testing, Injection and VersioningPrompt engineering for production: build a regression test set, defend against prompt injection, and version and release prompts safely, with runnable Python.5 min read

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