In this training, you will learn how to effectively integrate generative AI into all phases of your software development cycle - from requirements analysis to operations. You will learn how to achieve more with your existing team and the power of AI while taking risks and ethical aspects into account.
After completing the training, you will be able to use generative AI pragmatically and purposefully in your processes, implement your own ideas and make existing processes more efficient and innovative.
Agenda
- introduction & context
- Why foundation models and generative AI are shaping the future of software development
- Technological change and its concrete impact on your day-to-day work
- Clarify terms: LLMs, multimodal models, foundation models
- Requirements with generative AI
- AI-supported creation, analysis and refinement of requirements
- Generating user stories from natural language and multimodal content
- Automatically translate customer needs into structured requirements
- AI-supported classification and prioritization of requirements
- Create prototypes faster and effectively involve non-technical team members
- design architecture efficiently
- Automated derivation of system architectures from requirements
- Create software design proposals using generative AI
- Reflecting and documenting architectural trade-offs
- Agent-based simulation and evaluation of architecture variants
- Accelerate implementation
- Increase efficiency from code completion to AI-driven implementation of complete features
- Support for API integration and library usage
- Increase code quality through automated refactoring recommendations and documentation
- Understand and modernize legacy code faster
- optimize testing & quality assurance
- Generate tests automatically: Unit, integration and end-to-end
- Create synthetic test data and identify edge cases
- AI-supported reviews for test coverage and quality
- Make CI/CD more efficient
- Generate CI/CD configuration automatically (GitHub actions, GitLab pipelines)
- Automated creation of release notes and changelogs
- AI-based security scans and compliance checks
- Self-healing pipelines and automated troubleshooting
- improve operation & monitoring
- Accelerate incident management with AI-based root cause analyses
- Automated monitoring and prioritization of alerts
- AI-based log analysis and integration with observability stacks
- Strengthen maintenance & further development
- Automated bug fixing and ticket management
- AI-based explanations of code changes (“Diff Explainers”)
- Automatic detection of regressions and technical debt reduction
- Integrate ethics and compliance
- Raising awareness of bias and fairness in generative AI
- Ensure transparency and explainability
- Overview of regulatory requirements (EU AI Act / German AI Regulation)
- Understanding and taking into account risk classes of AI applications
Audience
Software developers, architects, enabling teams
Training Objectives
You can integrate generative AI into your software development process in a targeted manner and understand the added value it offers at every stage.
You will be able to analyze, structure and implement requirements more efficiently.
You can use generative AI to optimize architecture decisions and make them faster.
You are proficient in the use of AI tools to speed up the implementation and maintenance of your code.
You recognize and address risks and ethical challenges in the application of generative AI.
You are familiar with regulatory requirements and can make your AI projects compliant.
Your Trainers
Marco Steinke
INNOQ
Software architecture, AI
- Generative AI in Software Development Lifecycle
Marco Steinke is a consultant at INNOQ. His focus is on software architecture. He also deals with artificial intelligence, particularly the architecture and integration of AI systems.
Ole Wendland
INNOQ
Sustainable, future-oriented architecture; LLMs
- Generative AI in Software Development Lifecycle
Ole is a Senior Consultant and Software Architect at INNOQ in Switzerland. With his broad experience in software projects, he combines technical expertise with a deep understanding of the challenges faced by modern enterprises. His focus is on translating business requirements into sustainable, future-oriented solutions. As an all-rounder, Ole feels at home across the entire stack and continuously expands his spectrum of competencies. Along with his solid backend and frontend experience, he is deeply involved with Large Language Models (LLMs) and innovative applications of Foundation Models. Ole sees great potential in these technologies to optimize business processes and unlock new value creation opportunities for clients.
Roman Stranghöner
INNOQ
Conception and implementation of digital products
- Generative AI in Software Development Lifecycle
Roman works as a senior consultant and developer for INNOQ Germany. He builds web applications, preferably in agile teams and is focused on frontend related aspects like responsible use of web technologies, application architecture and tooling. His current interest lies in accessibility, responsive web design and user experience.