AI TRAINING

GitHub Copilot for Development Teams

Equip developers to ship faster and safer code using GitHub Copilot across real workflows.

Format: workshop
Duration: 12–16h
Level: practitioner
Group size: 6–20
Price / participant: €600–€1K
Group price: €6K–€18K
Audience: Software engineers, tech leads, and engineering managers already writing code daily
Prerequisites: Comfortable writing code in at least one language (Python, JavaScript, Java, or similar); basic Git workflow knowledge

What it covers

This hands-on programme teaches engineering teams to integrate GitHub Copilot effectively into daily development workflows using VS Code and JetBrains IDEs. Participants learn prompt crafting for code generation, how to use Copilot Chat for debugging and documentation, and how to embed security review habits when accepting AI suggestions. The course covers common failure modes — such as accepting hallucinated APIs or insecure patterns — and establishes team-wide standards for responsible Copilot use. Format combines live coding sessions with guided exercises and a team retrospective on adoption patterns.

What you'll be able to do

Configure GitHub Copilot in VS Code or JetBrains and tailor it to a team's codebase conventions
Write effective inline prompts and Copilot Chat queries that produce production-usable code suggestions
Identify and reject insecure, hallucinated, or licence-infringing code suggestions using a structured review checklist
Use Copilot to generate unit tests, docstrings, and PR descriptions that meet team quality standards
Define and document a team-level Copilot usage policy covering security, IP, and code review gates

Topics covered

Setting up and configuring Copilot in VS Code and JetBrains IDEs
Prompt engineering for accurate, context-aware code suggestions
Using Copilot Chat for debugging, refactoring, and documentation generation
Detecting and mitigating insecure or hallucinated code suggestions
Writing tests and reviewing pull requests with Copilot assistance
Understanding Copilot's training data, licensing, and IP considerations
Establishing team-wide conventions and acceptance policies
Measuring productivity impact and quality metrics post-adoption

Delivery

Delivered as a two-day in-person or live-virtual workshop (2 × 6-8 hours). Approximately 70% hands-on coding exercises on participants' own machines with real or sanitised codebases. Includes a shared Copilot-enabled GitHub repository for exercises, a security review checklist, and a team policy template. Remote delivery uses VS Code Live Share or JetBrains Space for collaborative exercises. A follow-up async Q&A channel is recommended for 30 days post-training.

What makes it work

Pairing Copilot adoption with updated code review checklists that explicitly flag AI-generated sections
Running a team retrospective after the first sprint using Copilot to calibrate acceptance norms
Defining a shared prompt library for recurring patterns (tests, migrations, API clients) to improve consistency
Tracking acceptance rate and post-merge defect rate to objectively measure productivity and quality impact

Common mistakes

Accepting Copilot suggestions without reviewing for hallucinated library names or deprecated APIs
Ignoring IP and licence implications when Copilot reproduces training data verbatim
Using Copilot as a replacement for code review rather than a first-pass assistant
Failing to establish team conventions, leading to inconsistent acceptance patterns and technical debt

When NOT to take this

A team that has not yet standardised its Git branching and code review process should address those foundations first — Copilot accelerates existing workflows but amplifies inconsistency in teams without baseline engineering hygiene.

Providers to consider

Sources

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