Prompt Engineering for Developers
Most developers treat prompting as trial and error. In this module, you'll learn a repeatable system for getting the output you need, every time.
What You'll Learn
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1
The Anatomy of an Effective Prompt System role, task, context, constraints, output format
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2
Context Injection Patterns How to give AI tools the right context without overloading the window
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3
Chain-of-Thought for Code Make the model reason step-by-step before generating code
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4
Prompt Templates & Snippets Build a reusable prompt library for your workflow
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5
Iterative Prompting Treating prompt refinement like a feedback loop
Capstone Project: Prompt Library CLI
Build a command-line tool that stores, retrieves, and applies your personal prompt templates to real coding tasks.
Skills: Python, CLI design, templating, version control
Why This Matters for Your Career
Prompt quality directly determines output quality. Engineers who can write precise, reproducible prompts ship faster, debug less, and have more control over AI-generated code than those who rely on luck.