AI for Software Developers Training – Rethinking Development, Ensuring Quality, and Harnessing Potential
Upon request
1–2 days
Munich, Online, or In-House
Upon request
German or English
Digital Certificate of Participation
From requirements to production-ready code: Artificial intelligence as a support tool for modern development teams. Software development is on the verge of a fundamental transformation. AI systems can explain code, develop solution variants, create tests, and assist with debugging. At the same time, new requirements for quality, security, and technical accountability are emerging. Developers must therefore not only know which tools are available but also be able to assess when AI provides real added value and when human expertise remains indispensable. This training course demonstrates how artificial intelligence can be meaningfully integrated into the entire software development process. You will learn how to guide AI systems effectively, critically evaluate results, and understand modern approaches such as Vibe Coding, Agentic Coding, RAG, and MCP. The focus is on practical application in a developer’s day-to-day work—from requirements analysis through coding and testing to code reviews, security, and maintenance.
Course Content and Structure of the "AI in Software Development" Training Course
This training program combines technological fundamentals with practical applications throughout the software life cycle. The content can be tailored to the team’s development environment, programming languages, and level of experience. (Please contact our experts to discuss customization.)
- Classification of AI, Large Language Models, Generative AI, Data Science, Big Data, and Automation
- How Generative AI Systems Work, Their Strengths, and Their Limitations
- Changes in Typical Developer Tasks and Work Practices
- Realistically Assess Current AI Capabilities and Identify Misconceptions
- Overview of Relevant Tools and Development Trends
- Opportunities, Risks, and Impact on Roles and Teams
Course Outcome
: A solid understanding of the technological foundations and the significance of AI for software development.
- Support with requirements analysis, user stories, and technical documentation
- Using AI for Software Design, Architecture, and Solution Options
- Code generation, code completion, and support for standard tasks
- Have source code explained, analyzed, and documented in a clear and understandable way
- AI-powered debugging and systematic troubleshooting
- Test Case Generation, Test Data, and Quality Assurance
- Code reviews, refactoring, maintenance, and further development of existing systems
Outcome: Participants can apply AI in a targeted manner during various phases of the software life cycle.
- Formulate clear and verifiable tasks
- Provide relevant requirements, code snippets, and system contexts
- Structure Roles, Objectives, Constraints, and Acceptance Criteria
- Working Iteratively with AI: Analyze, Generate, Test, and Improve
- Developing Reusable Prompts for Coding, Debugging, Testing, and Reviews
- Understanding the Impact of Context Quality and Requirements on the Quality of Results
Outcome: Safe and structured use of AI systems for better and more transparent development results.
- Classifying Vibe Coding and Natural Language as a Development Interface
- Applications for prototyping, small tools, and in-house applications
- Fundamentals of Agent-Based Development Processes and AI-Supported Task Decomposition
- Interaction between AI assistants, the development environment, the repository, and testing processes
- Opportunities and Limitations of Autonomous Code and Development Workflows
- Impact on Roles, Responsibilities, and Collaboration in Development Teams
- Transition from prototypes to solutions that can be maintained and operated professionally
Result
: Understanding modern AI-driven development approaches and their practical limits.
- Critically Review AI-Generated Code and Take Professional Responsibility for It
- Evaluate quality, maintainability, traceability, and technical debt
- Dealing with Hallucinations, Incorrect Suggestions, and Incomplete Context
- Take into account security risks, data protection, and licensing and copyright issues
- Securely Handling Source Code, Login Credentials, and Company Information
- Develop best practices for reviews, testing, approvals, and human checkpoints
- Define custom operational rules and specific AI workflows for developers’ day-to-day work
Result
: Responsible and quality-oriented use of AI in software development.
Tangible Results – What You Can Put into Practice Right Away After the AI for Software Developers Training Course
After the training, you will be able to use AI strategically in development processes and better assess the quality of the results.
- Use AI tools for requirements analysis, coding, testing, and documentation
- Generate, explain, analyze, and improve code
- Systematically Investigate and Resolve Errors Using AI
- Create test cases, test data, and documentation more efficiently
- Developing Reusable Prompts for Development Tasks
- Check AI-generated code for quality, security, and maintainability
- Identify technical debt and flawed AI suggestions early on
- Seamlessly Integrate AI into Existing Development Environments and Team Processes
- Establish Custom Rules for the Responsible Use of AI Within the Team
Who is the AI for Software Developers training course intended for?
This continuing education program is intended for:
- Software developer
- Technical Leads and Engineering Managers
- Software Architects
- DevOps and Platform Teams
- Testers and Quality Assurance Specialists
- Product Owners and Technical Project Managers
- Development teams that want to use AI productively
- Companies that want to establish modern AI-driven development processes
The training is suitable for both developers with some experience in AI and teams that have had little experience working with AI tools so far.
Prerequisites for the AI for Software Developers Training Course
A basic understanding of software development or technical projects is helpful. Programming skills are recommended for participation, as much of the content relates directly to code, development processes, and technical artifacts.
Custom AI Training for Development Teams
We design customized in-house training programs—tailored perfectly to your needs.
- Customized training for developers, architects, and technical teams
- Integration of proprietary programming languages, tools, and development environments
- Working with real-world code examples and existing systems
- Development of Custom Prompt Libraries and AI Workflows
- Offered as a seminar, workshop, or in-house training session
About Us as a Training Provider
We are an experienced management consulting firm with expertise in digital transformation, software development, and artificial intelligence. Our training courses combine technological fundamentals with specific requirements from development projects and modern engineering environments. We help companies safely implement AI tools, refine development processes, and empower teams to adopt new ways of working. In doing so, we focus not only on the speed of code development but also on quality, maintainability, security, and sustainable technical implementation.
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Expertise in AI and Software Development – Your Trainer
Thorsten Müller brings together expertise in digital transformation, modern development approaches, and artificial intelligence. He explains complex topics such as generative AI, prompt engineering, vibe coding, and agent-based development workflows in an accessible way, with direct relevance to real-world practice. In doing so, he demonstrates how development teams can meaningfully integrate AI into existing processes, critically evaluate results, and maintain technical ownership. The focus is on methods and workflows that accelerate development, ensure quality, and improve collaboration between business experts, developers, and management.
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- Using AI Specifically for Software Development and Engineering
- Customized Training for Developers and Technical Teams
- Proven expertise in AI, development processes, and digital transformation
- Practical Methods for Coding, Debugging, Testing, and Code Quality
TISAX and ISO certification apply only to the Munich location
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TISAX and ISO certification apply only to the Munich location
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FAQ – Frequently Asked Questions About AI in Software Development Training
A basic understanding of software development is helpful and recommended for this training course. The course content covers, among other topics, code generation, debugging, testing, architecture, and technical documentation. Participants without programming experience can understand the fundamentals but will benefit most if they have a technical work background.
AI can assist with requirements analysis, user stories, software design, code generation, and documentation. It can also help prepare for debugging, test case generation, refactoring, and code reviews more efficiently. This training course demonstrates how to effectively integrate this support into existing development processes.
Vibe Coding refers to the development of applications using natural language and AI-powered tools. Requirements, functions, and changes are described, and the AI uses this information to generate code or application components. In this training, you’ll learn when this approach is suitable for prototypes and smaller applications—and when professional development remains necessary.
Agentic coding refers to development processes in which AI agents independently structure, process, and, in some cases, verify more complex tasks. For example, they can analyze code, suggest changes, or prepare tests. This training course covers the opportunities, limitations, and necessary control points of such workflows.
AI-generated code must always be reviewed, tested, and evaluated by experts. In this training, you will learn how to use AI to support code reviews, test cases, debugging, and iterative improvements. The course also covers maintainability, technical debt, security, and traceability.
Data protection and the secure handling of technical information are integral parts of the training. You’ll learn what to look out for when dealing with source code, login credentials, company information, and external AI tools. Licensing and copyright issues, as well as human approvals, are also covered.
Yes. In-house training sessions allow you to incorporate your own code examples, development environments, and processes. This enables you to apply prompts, reviews, and AI workflows directly to your projects. Confidential information should only be processed on approved systems.
Many methods can be implemented immediately, such as prompts for code analysis, debugging, testing, or documentation. This enables development teams to quickly improve their initial workflows. For production-critical applications, the approaches developed should then be validated through technical standards, reviews, and approval processes.