AI-Assisted Software Engineering

AI can speed up development work. However, speed alone does not make software any better or more cost-effective. What matters is how requirements are formulated, results are verified, risks are managed and technical decisions are validated.

This remote training course shows you how to use AI productively and responsibly throughout the entire software lifecycle: from requirements engineering and architecture through implementation and refactoring to testing, review, documentation and maintenance.

The focus is not on tool demonstrations or short-term productivity gains. You will learn working methods that transform plausible suggestions into transparent and robust engineering outcomes.

• The potential and limitations of AI in software engineering
• Potential applications throughout the entire software lifecycle
• AI in requirements engineering, architecture and software design
• Support for implementation, code comprehension and refactoring
• Effective prompting for analysis, design, coding, testing and review
• Spec First, an iterative approach and small, verifiable steps
• Test design and quality assurance for AI-generated results
• Guidelines and standards for teams and organisations
• Economic benefits, follow-up costs and measurable impact

This remote training course combines concise technical insights with practical exercises, group reflection and the application of what you’ve learnt to your day-to-day development work.

You will explore the potential and limitations of AI, suitable areas of application within the software development lifecycle, and effective working and prompting patterns. In a detailed practical session, you will apply these to requirements, existing code and refactoring tasks.

Furthermore, the focus is on testing, review and the systematic validation of AI-generated results. You will also identify the security measures, quality standards and organisational guidelines required for productive use within a team.

For the exercises, you can work with the examples provided or – provided the circumstances allow – with situations from your own work context.

The training is aimed at software developers, tech leads, software architects, QA managers, engineering managers and product managers with close links to software development.

It is particularly suitable for you if you want to do more than just experiment with AI on an ad hoc basis, but wish to integrate it into your development work in a structured, secure manner and with clear quality standards in mind.

You should have experience with modern software development and typical processes in software engineering. Personal programming experience is helpful, as some of the exercises cover requirements, code, tests and reviews.

In-depth prior knowledge of artificial intelligence or specific AI tools is not required.

• you’ll be able to assess tasks within the software life cycle to determine whether and how they are suitable for AI support
• you will formulate requirements, context, quality criteria and constraints in such a way that the results are traceable and verifiable
• you will use AI as a sparring partner for analysis, design, implementation, refactoring, testing and review
• you work iteratively and in small steps, rather than allowing complete solutions to be generated uncontrollably
• you validate AI-generated output through testing, reviews, architectural principles and clear approval criteria
• You develop initial guidelines and team standards for the responsible use of AI
• You evaluate the use of AI not only from a technical perspective, but also in terms of productivity, rework, follow-up costs and economic benefits