GitHub: Advanced Prompt Engineering for Code
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GitHub: Advanced Prompt Engineering for Code
This course is part of Mastering GitHub Specialization
Instructor: Alfredo Deza
Included with
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Recommended experience
Recommended experience
What you'll learn
Structure multi-turn GitHub Copilot conversations that build context incrementally and produce more accurate code than single-shot prompts
Apply iterative refinement techniques like scope narrowing, error correction, and follow-up prompting to transform code into production-ready output
Leverage cross-file context, open editor tabs, and specification-driven generation to work effectively across large and unfamiliar codebases
Skills you'll gain
Tools you'll learn
Details to know
March 2026
2 assignments
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There are 3 modules in this course
Move beyond basic code completions and learn to use GitHub Copilot as a conversational development partner. This course teaches you how to structure multi-turn interactions that build context incrementally, producing more accurate and relevant code than single-shot prompts. You will master iterative refinement techniques that transform rough initial outputs into production-quality code through structured follow-up prompts and scope narrowing.
The course covers the three Copilot interaction modes β Ask, Edit, and Agent β and when to use each for maximum effectiveness. You will learn to control context precisely using chat inputs like @workspace references, #file markers, and open editor tabs. Advanced topics include generating API documentation directly from code, creating implementation code from API specifications, and navigating unknown codebases using structured Copilot conversations. Each technique is demonstrated with real-world projects in Visual Studio Code, giving you practical patterns you can apply immediately to your own development workflow. A capstone project synthesizes all techniques into an end-to-end AI-assisted development scenario.
Learn the core skills of advanced prompt engineering
What's included
10 videos4 readings1 assignment
10 videosβ’Total 51 minutes
- Course Introductionβ’1 minute
- Crafting Multi-Turn Promptsβ’4 minutes
- Iterative Refinement Techniquesβ’6 minutes
- Challenge: Refactor with Conversationβ’7 minutes
- Solution: Refactor with Conversationβ’6 minutes
- Analyzing Your Conversational Approachβ’5 minutes
- From Basic to Conversationalβ’5 minutes
- The Power of Multi-Turn Interactionsβ’5 minutes
- Overview of Chat Inputsβ’6 minutes
- Overview of Modesβ’7 minutes
4 readingsβ’Total 40 minutes
- Key Terms: Iterative Refinement Techniquesβ’10 minutes
- Reflection: Iterative Refinement Techniquesβ’10 minutes
- Key Terms: Conversational Prompting Fundamentalsβ’10 minutes
- Reflection: Conversational Prompting Fundamentalsβ’10 minutes
1 assignmentβ’Total 30 minutes
- Conversational Promptingβ’30 minutes
Covers key details of context and working with large projects
What's included
5 videos2 readings
5 videosβ’Total 27 minutes
- Providing Context from Multiple Filesβ’5 minutes
- Leveraging Open Files for Accuracyβ’6 minutes
- Generate API Docs from Codeβ’5 minutes
- Generate Code from API Documentationβ’6 minutes
- Working with Unknown Codebasesβ’6 minutes
2 readingsβ’Total 20 minutes
- Key Terms: Context Management and Code Generationβ’10 minutes
- Reflection: Context Management and Code Generationβ’10 minutes
Putting it all together. Learning to use prompts and context to build advanced solutions
What's included
1 video1 reading1 assignment
1 videoβ’Total 2 minutes
- Course Conclusionβ’2 minutes
1 readingβ’Total 10 minutes
- Next Stepsβ’10 minutes
1 assignmentβ’Total 15 minutes
- Advanced Promptingβ’15 minutes
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Frequently asked questions
No prior Copilot experience is required. The course starts with foundational conversational prompting concepts before progressing to advanced multi-file context techniques. Basic familiarity with any code editor is sufficient.
Yes. Most developers use Copilot for single-shot completions and miss the conversational capabilities. This course teaches multi-turn interaction patterns, iterative refinement, and context management strategies that significantly improve the quality and relevance of generated code.
Demonstrations use Python with frameworks like FastAPI, but the prompting techniques apply to any language supported by GitHub Copilot. The focus is on interaction patterns and context strategies rather than language-specific syntax.
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