GitHub Copilot and AI-Assisted Development
AI-assisted development as a team workflow: reviewing AI-generated code, testing, and security — not just an introduction to the GitHub Copilot tool.
Copilot changes the team’s workflow, not just the code editor
Bringing GitHub Copilot into a team is more than installing an IDE plugin — it changes the code review process, accountability for code, and how new developers are onboarded. This training skips basic tool usage (covered in other catalog entries) and focuses on how to organize team work around AI-assisted development.
Reviewing code no one on the team wrote
When part of a pull request comes from a Copilot suggestion, a standard review checklist stops being enough. Participants build an extended verification checklist — business logic, edge cases, compliance with team standards — and establish who is accountable for a bug that passed review even though AI generated it.
Security and quality at higher speed
Writing code faster cannot mean more security vulnerabilities or growing technical debt. The workshop covers common vulnerabilities in AI-suggested code, SAST tools for automated verification before merge, and how to build team guidelines defining acceptable AI use in production code.
Benefits
- Use GitHub Copilot effectively in daily development workflow
- Establish code review rules for code co-written with AI
- Identify and mitigate security risks in Copilot-generated code
- Build tests that verify AI suggestions before merge
Who is this training for?
Prerequisites
- Programming experience in any language
- Basic knowledge of the Git version control system
Training program
GitHub Copilot in daily workflow
- Configuring Copilot in your IDE and tuning suggestions to project style
- Copilot Chat, Copilot CLI, and using AI beyond code autocompletion
- Prompting techniques in a code context — comments, naming, file context
- When to trust a Copilot suggestion and when to reject it
Code review for AI-assisted code
- How the code review process changes when part of the code was written by AI
- A verification checklist: business logic, edge cases, compliance with team standards
- Flagging and tracking AI-generated code in the repository
- Accountability for code — who is responsible for a bug generated by Copilot
Security of AI-generated code
- Common vulnerabilities in AI-suggested code (SQL injection, hardcoded secrets)
- Scanning AI-generated code with SAST tools before merge
- Licensing risk — models' training data and open-source license compliance
- A team policy for acceptable AI use in production code
Testing and quality in an AI-assisted development team
- Building unit tests that verify Copilot's suggestions
- Team productivity metrics before and after adopting Copilot
- Onboarding new team members to working with AI tools
- Establishing shared team guidelines for AI-assisted development
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from GitHub Copilot — AI in Programming and GitHub Copilot for Developers — Productive Coding with AI in our catalog?
Those courses are an introduction to the tool itself for an individual developer. This training treats AI-assisted development as a team workflow — reviewing AI-generated code, testing, security, and shared team guidelines — rather than just teaching how to operate Copilot.
Is code suggested by Copilot safe to deploy without changes?
Not always — AI models can suggest code with vulnerabilities (e.g. missing input validation, hardcoded secrets) or patterns inconsistent with the open-source licenses used in training. In the training we show concrete checklists and SAST tools for verifying code before merge.
How do I adapt the code review process to account for code written with Copilot?
The key is flagging AI-generated sections and extending the review checklist with AI-specific questions: is the business logic correct, are edge cases covered, does the code follow team standards. In the workshop we build such a checklist together with participants.
Does AI-assisted development actually increase team productivity?
It depends on how the team uses it — without a code review process and tests, Copilot can speed up writing code while increasing technical debt. In the training we show how to measure real productivity (not just lines of code) and how to implement a process that maintains quality alongside faster development.
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