This course focuses on advanced, real-world GitHub Copilot workflows for developers. Participants will deepen their use of GitHub Copilot inside VS Code and apply repository-level customization and context engineering. Microsoft Copilot and broader context-engineering concepts are covered briefly and purposefully, primarily to support developer workflows such as incident response, meeting summaries, and email triage.
Prerequisites:
In order to succeed in this course, you will need:Â
- Intermediate coding skills in Java
- Familiarity with GitHub, Git workflows, and VS Code
- A GitHub Copilot-enabled account
- Access to Microsoft Copilot
NOTE: Pluralsight requires each participant to have access to the Generative AI tool(s) used in this course. Where Customer does not provide that access, Pluralsight will provide it and AI lab fees will apply.
Purpose
| Advance the use of GitHub Copilot inside VS Code |
Audience
| Engineers and developers moving to autonomous agentic workflows and standardized AI context engineering |
Role
| Software Developers | Java Programmers | Technical Managers |
Skill level
| Intermediate |
Style
| Lecture | Hands-on Activities | Labs |
Duration
| 2 days |
Related technologies
| Gen AI | Microsoft Copilot | Github | VS Code | Java |
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Learning Objectives
- Use GitHub Copilot in VS Code with advanced capabilities, including agent mode and multi-step task delegation
- Design agent-ready repositories using repository-specific custom instructions to standardize AI behavior across teams
- Shape GitHub issues and tasks so they can be safely and effectively executed by autonomous coding agents
- Assign work to the GitHub Copilot coding agent, review agent-generated pull requests, and iterate through feedback loops
- Create and use custom agents in VS Code to act as specialized teammates
- Apply prompting patterns as task specifications in agentic workflows
- Integrate Microsoft Copilot selectively into developer workflows using Outlook and Teams
- Engineer durable context artifacts that improve agent reliability and reduce prompt sprawl
- Apply agent governance practices, including CI enforcement, code review strategies, and verification-first workflows
- Combine multiple AI tools intentionally to improve code quality, delivery velocity, and long-term maintainability without over-automation