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An advanced, practice-driven course for developers already using AI coding tools. The focus is working faster and safer with Microsoft Copilot by mastering prompt patterns, context strategies, and integration with real repositories, tests, and CI.
You will refine how you scope engineering tasks, design prompts, and steer Microsoft Copilot to produce reliable code changes. You will connect prompts to your repos and workflows, apply guardrails with tests and policies, and adopt lightweight evaluation so quality improves over time.
After this training you will be confident in:
• Framing engineering tasks for Copilot and selecting effective prompting strategies
• Using repository context, symbols, configs, and documentation to ground outputs
• Driving code edits, explanations, and diffs across multi-file projects
• Tightening feedback loops with tests and acceptance criteria
• Integrating Copilot use into Git, code review, and CI
• Managing privacy, security, and IP considerations with clear auditability
• Strong command of Git and code review practices
• Familiarity with a CI system and package managers in at least one language
• Access to a non-sensitive repository suitable for training
• Microsoft Copilot enabled in your IDE or GitHub account as applicable
*We customize the course outline and content to your specific needs and relevant use cases.
Module 1: Copilot in the software development lifecycle
• When Copilot accelerates planning, coding, testing, and review
• Limits of AI assistance and when to hand back to humans
• Task decomposition, constraints, and acceptance criteria as prompt inputs
• Reading model outputs critically and deciding when to iterate or restart
Module 2: High quality prompts for real codebases
• Patterns for feature additions, bug fixes, migrations, and docs
• Requesting focused diffs, patch style outputs, and commit messages
• Style, lint, and formatting rules expressed as constraints
• Capturing invariants and non functional requirements inside prompts
Module 3: Preparing and managing context
• Selecting the right files, symbols, logs, and configs without overload
• Summarizing large areas of code and linking design docs or API references
• Grounding with issue descriptions, changelogs, and READMEs
• Keeping prompts concise while preserving critical constraints
Module 4: Tests as guardrails and debugging with Copilot
• Writing or tightening unit and integration tests before edits
• Using failing tests and stack traces to steer the next iteration
• Producing reviewer friendly explanations and change notes
• Simple strategies to prevent regressions across modules and services
Module 5: Copilot integrated with Git and CI
• Branching approaches that complement AI assisted changes
• Pull request descriptions, risk callouts, and review checklists
• Connecting prompts to automated tests and quality gates
• Tracking prompt inputs and outputs for traceability
Module 6: Prompt templates and team reuse
• Reusable scaffolds for common workflows in your stack
• Embedding coding standards, security checks, and performance expectations
• Organizing a prompt library and keeping it versioned
• Sharing patterns across teams without leaking sensitive context
Module 7: Security, privacy, and IP for AI assisted code
• Redacting secrets and minimizing sensitive data sharing
• License awareness and provenance for generated code
• Decision logs, audit trails, and stakeholder visibility
• Aligning usage with internal policies and client commitments
Module 8: Evaluation and continuous improvement
• Lightweight metrics such as pass rates, rework, and cycle time
• A and B style prompt comparisons and small experiments
• Building a team playbook for safe rollout and maintenance
• Roadmap for scaling Copilot usage while keeping humans in control
Hands-on learning with expert instructors at your location for organizations.
Master new skills guided by experienced instructors from anywhere.
