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Keeping the Workshop Current

GitHub Copilot changes too quickly for a static workshop to maintain a reliable product roadmap. Instead of predicting future features, this page gives trainers and maintainers a repeatable way to keep the workshop aligned with the current Copilot experience.

What to review before each delivery

Area What to check
Copilot UI Mode names, chat controls, tool approval prompts, and screenshot accuracy.
Models Current model names, Auto behavior, visual-input support, and organization policy.
MCP VS Code MCP configuration, server startup UX, and tool enablement screens.
Lab code Dependencies, APIs, build commands, tests, and runtime versions.
External labs Repository availability, instructions, QR codes, and prerequisites.
Security guidance Token handling, generated-code review, dependency review, and customer data boundaries.

Current capability areas to watch

  • Agentic coding workflows: multi-file changes, terminal commands, tests, and tool use with review.
  • MCP servers: bringing external tools and data sources into Copilot Chat.
  • Model selection: Auto model selection, specialized reasoning models, and visual input support.
  • Enterprise controls: policy, auditability, content exclusion, and organization-level model availability.
  • Repository context: how Copilot uses open files, selections, workspace search, issues, pull requests, and docs.
  1. Review the official Copilot docs and changelog.
  2. Run the selected labs from a clean clone.
  3. Update prompts and screenshots when the product UX changes.
  4. Replace hard-coded model names with task-based guidance unless a lab requires a specific model.
  5. Build the MkDocs site and fix broken links or assets.
  6. Commit changes in the fork and open a pull request to the Azure-Samples repository.

Official sources

Guidance for trainers

When a participant sees a slightly different UI, treat it as normal. Copilot capabilities can vary by plan, organization policy, extension version, IDE, and rollout status. Focus on durable practices: provide context, ask for small iterations, inspect diffs, run tests, and keep humans in control of accepted changes.