GitHub Copilot Training for Development Teams

Copilot now covers four different surfaces: inline suggestions, Copilot Chat, code review, and the coding agent. Each one sits next to your team's wider AI-assisted development practice, with expectations set before rollout starts.

• Copilot Business vs. Enterprise: what each tier includes
• The IDE, chat, review, and agent surfaces, and what each one is for
• How Copilot differs from a general-purpose AI coding assistant
• What it's good at, and where a developer still has to decide
• Setting expectations before a team-wide rollout begins

Configure Copilot correctly at the organization level, including seats and roles as well as the policies that decide what the tool can see and suggest. Then write the repository instructions that keep suggestions lined up with your codebase.

• Assigning seats and access levels across the organization
• Organization policies for content exclusions and public-code matching alongside chat controls
• Deciding what a policy should say before you write it
• Writing repository custom instructions that reflect your team's conventions
• Connecting Copilot to your existing identity and access controls
• Keeping policy and instructions current as the organization grows

A repeatable way to work with Copilot day to day: reach for inline suggestions when they help, and Copilot Chat when a task needs back-and-forth. Practiced on your team's own code in your own IDE.

• Choosing inline suggestions or Copilot Chat for a given task
• Giving Copilot Chat the right file and repository context
• Explaining and fixing code, then generating tests through chat-driven workflows
• Prompting patterns for code
• Working across a multi-file change without losing the thread
• Building a shared library of prompts that work for your stack

Copilot can make an automated first pass on a pull request and suggest changes. Its agent can then pick up a scoped issue and open its own pull request. The group decides where each fits into a review process the team already trusts.

• Automated review, first pass
• Assigning a scoped issue to the coding agent and reviewing what comes back
• Reading an agent-opened pull request as critically as a colleague's
• Fitting Copilot's review output into your existing CI and approval process
• Deciding which tasks are safe to hand to an agent, and which aren't

Extending Copilot past the editor: using it from the terminal for everyday tasks, and writing the repository instructions that keep every developer's suggestions consistent with your codebase instead of their own habits.

• Copilot from the terminal
• Writing repository instructions that cover conventions and boundaries with examples
• Building a shared instructions library across multiple repositories
• Keeping instructions current as the codebase and conventions change
• Extending Copilot into scripts and other touch points around your workflow

From scattered individual use to a team that runs GitHub Copilot well across the organization. The path starts with a pilot and accounts for the security and IP boundaries specific to Copilot's data flows. It also names the one metric worth watching on the way to a wider rollout.

• Choosing a pilot team and building a champions network around it
• Writing a Copilot usage policy engineers will follow under deadline
• Security, IP, data boundaries
• Metrics that show real adoption across the team
• A rollout plan you leave with, built on your own organization's policies