AI/ML 
Software Development 
Github Copilot
Copilot Workshop and Implementation
What can GitHub Copilot do?
- provides intelligent code suggestions
- significantly reduces repetitive tasks
- automatically refactors code
- quickly creates test cases
- uses configuration files to maintain coding standards
- generates comprehensive documentation
You receive tailored, real-time code recommendations based on your project and coding style. You can describe code in natural language and simple words, and Copilot will translate it into code. At the same time, the AI automatically suggests improvements for structure and readability.
What do you gain as a developer? Copilot in numbers!
Increased productivity:
55% faster task completion
46% of code automated
78% successful task completion
Improved code quality:
Up to 55% fewer bugs
Up to 15% faster code reviews
Greater consistency
Accelerated learning:
Faster onboarding with Copilot code snippets
Satisfaction:
60–75% feel less frustrated
87% report more energy
73% say Copilot helps them stay in flow
Metodology 4D
Discovery
Understanding the DNA of the problem before the first line of code is written
The Discovery phase allows for a deep understanding of the business challenge, user needs, and technological context. As a result, the project starts on solid foundations, and key assumptions are validated before development begins.
Key artifact
Standardized Concept Document
Our focus
Our activities
We analyze the client’s business and technological environment: from existing systems to user needs and strategic goals. We validate business assumptions, identify risks, and define the problem to be solved. The outcome is a coherent product concept that forms the foundation for the next stages of the project.
Definition
Translating knowledge and ideas into a concrete product plan
In the Definition phase, we transform insights from Discovery into a detailed solution design. This includes defining requirements, system architecture, and the user experience concept.
Key artifact
Product & Architecture Blueprint
Our focus
Our activities
We translate business goals into specific functional and technical requirements. We design UX prototypes, define the architecture, and plan the project implementation. This ensures development starts with a clear plan and minimal risk.
Delivery
Building and delivering reliable software
In the Delivery phase, we develop the final solution. We focus on code quality, clear communication with stakeholders, and a stable product release.
Key artifact
Production-ready product / deployment
Our focus
Our activities
Our teams build the solution using modern development practices and continuous integration. We regularly test the product and maintain transparent communication with stakeholders to deliver a stable, production-ready solution.
Direction
Transforming a product into a growing digital business
Direction is a phase of long-term product development. Instead of ending cooperation after implementation, we support clients in scaling solutions, introducing innovations, and building a competitive advantage.
Key artifact
Product development and innovation roadmap
Our focus
Our activities
Together with the client, we analyze product data, identify new growth opportunities, and plan future functionalities. We help scale the solution, optimize its performance, and build a long-term product strategy.
Workshop for Preparing for GitHub Copilot
In a preparation workshop for GitHub Copilot, we demonstrate the core features and discuss best practices to unlock the full potential of this powerful tool. In the hands-on session, you can watch our developer code in real time and see how GitHub Copilot supports them by, for example:
- generating and improving unit and integration tests,
- creating technical documentation with minimal effort,
- suggesting AI-powered code refactoring.
Sample Agenda
Our clients especially value these live sessions, as they provide a realistic view of working with GitHub Copilot in practice. A possible workshop flow could include:
- Welcome and introduction
- Legal risks of GitHub Copilot and similar tools
- What is Prompt Engineering and how does it work?
- GitHub Copilot – functionality and licensing
- Live coding session with IntelliJ and VS Code
- GitHub Copilot Enterprise features overview, preview, and alternatives
- Development performance metrics
- Optional demonstration
- Q&A and wrap-up
GitHub Copilot Workshop: Structure and Methodology
The contents of the GitHub Copilot course are tailored to your specific needs. After a short welcome and introduction, we first address the legal risks of such tools to provide confidence in using Copilot and similar assistants. Then we dive right in!
Together, we tackle the question:
In our workshops, interaction is key to getting everyone on board and working toward the same goal. The exchange helps all participants stay engaged throughout the session and be well-prepared for upcoming tasks and challenges.
What is Prompt Engineering and how does it work? In this section, we emphasize the importance of Prompt Engineering and show participants how to effectively build prompts.
We provide practical tips and demonstrate different techniques before moving on to:
- Functionalities
- Licenses
- Workflows
Workshop Highlight
The highlight of the Copilot workshop is the Live Coding Session with IntelliJ and VS Code. We demonstrate code generation, explain functions and methods in detail, and generate complete files.
We also focus on explaining code, debugging (error detection and fixing), code refactoring, test case generation, and documentation (docstrings and comments).
To round off, we provide an overview and preview of GitHub Copilot Enterprise features and alternatives, along with insights into development performance metrics to optimize and better evaluate productivity, quality, and efficiency.
Optionally, you can choose a follow-up demonstration of ChatGPT and Claude in programming, or a practical workshop with chats.
The workshop concludes with a Q&A session, an open discussion, evaluation of a trainee with one year of AI-assistant experience, and a summary.
Implementing GitHub Copilot in the Company
The implementation of GitHub Copilot involves a set of preparation and execution measures to ensure that all elements interact smoothly and deliver the desired results. We support you at every stage, ensuring the implementation is a success.
We pay particular attention to the preparation phase, which starts with a GitHub Copilot course and a careful transition using a small pilot group before the full rollout.
Security and Compliance
In this sensitive area, we verify whether the use of GitHub Copilot complies with your company’s internal security and data protection guidelines. We also ensure that sensitive data is not passed on by the AI and that Copilot meets your compliance requirements.
Beyond checking general standards, additional security measures can be applied, such as restricting AI usage to specific areas.
Change Management and Employee Training
The core of change management and employee training should be transparent communication as well as mental and technical preparation for the upcoming changes.
AI will relieve employees from tasks that are often disliked and simultaneously support them in their core activities. As a result, productivity typically increases, along with overall job satisfaction. While AI will not replace a professional developer, it will become an active contributor to the programming process.
Pilot Phase and First Steps
Before the full rollout, a small group will test Copilot under controlled conditions in a secure test environment. This group will gather initial experience and provide regular feedback on the process.
In addition, there will be a training phase with introductory workshops to prepare the team. Training materials will be tailored to the real needs of your organization, and the phase will be accompanied by best practices for using Copilot.
Integration into the Existing Environment
Our task is to seamlessly integrate GitHub Copilot into your existing development processes and tools. We have developed a comprehensive concept that not only takes your individual needs into account but puts them at the center of our efforts.
We achieve this by supporting the configuration of your IDEs, connecting your company’s GitHub repositories, and ensuring that your developers’ workflow remains uninterrupted.
Technologies We Use
Best Practices and Use Cases
To ensure effective use of GitHub Copilot, we provide you with proven best practices and tailored solutions for your organization’s specific use cases.
The best practices we share are tried-and-tested methods and tips that enable developers to leverage Copilot purposefully and sustainably.
- Copilot supports you but does not replace developers – full control over the code remains with humans.
- It provides suggestions that can be interacted with to generate high-quality code.
- Using Copilot in a team setting makes sense and is especially effective.
- AI increases the consistency and speed of the entire development process.
Copilot can also be used for targeted applications, such as performing repetitive tasks like automated test writing. Other scenarios may include supporting developers in using new programming languages, quickly identifying errors (so-called debugging), or rapidly creating prototypes where the developer focuses on logic and functionality while Copilot handles code generation.