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An advanced, practice-driven course for developers already using AI coding tools. The focus is working faster and safer with GitHub 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 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 choosing effective prompting strategies
• Grounding generations with repository context, symbols, and documentation
• Driving code edits, explanations, and diffs across multi-file projects
• Tightening feedback loops with tests and acceptance criteria
• Integrating prompts 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
• Copilot enabled in your IDE and GitHub account
*We customize the course outline and content to your specific needs and relevant use cases.
Module 1: Copilot in the SDLC
• Where Copilot helps in planning, coding, testing, and revie
• Task decomposition, constraints, and acceptance criteria as prompt inputs
• Reading outputs critically and deciding when to iterate or restart
Module 2: Preparing high quality context
• Supplying the right files, symbols, configs, and logs without overload
• Summarizing large code areas and capturing invariants to protect
• Linking design docs and API references to raise answer quality
Module 3: Code generation and editing patterns
• Requesting focused diffs, patch-style outputs, and commit messages
• Stepwise prompts for adding a feature while preserving contracts
• Refactoring prompts that keep public APIs stable
Module 4: Tests as guardrails and debugging
• Writing or tightening tests before edits to bound behavior
• Using failing tests and logs to steer the next iteration
• Producing reviewer-friendly explanations and change notes
Module 5: Prompt templates and reuse
• Reusable scaffolds for bug fixes, feature stubs, and migrations
• Embedding style, lint, and formatting rules as constraints
• Team conventions for naming, structure, and checklists
Module 6: Git and CI integration
• Branching approaches that complement AI-assisted changes
• Pull request descriptions, risk callouts, and review checklists
• Connecting prompts to automated tests and quality gates
Module 7: Retrieval and external tools
• When to attach docs, tickets, and changelogs for grounding
• Selecting utility tools the model should rely on, such as linters and test runners
• Handling long-running tasks and chunked objectives
Module 8: Security, privacy, IP, and evaluation
• Redacting secrets and minimizing sensitive data sharing
• License awareness and provenance for generated code
• Decision logs and traceability, simple metrics for continuous improvement
Hands-on learning with expert instructors at your location for organizations.
Master new skills guided by experienced instructors from anywhere.
