Software Craftsmanship with Generative AI - Hands-On training for TypeScript or Java+Spring Boot Developers

Expert workshop is something more than a training. Workshop is focused on client's project.

Code: BigDataML-ai
Category: BigData, Streaming and Machine Learning
Format: 20% lecture / 80% workshop
Duration: 3 days
Target audience: developers
architects
Enrollment: Groups, companies
Venue: Client's office.

Artificial intelligence is no longer just a tool for generating code—it’s becoming a true partner in a developer’s workflow.

In this 3-day hands-on training, you’ll learn how to effectively leverage Generative AI in the spirit of Software Craftsmanship, with a strong focus on backend development using Java and Spring Boot.

Through a series of practical exercises, live coding sessions, and real-world use cases, you will:

  • Learn how to collaborate with AI to write clean, maintainable, and well-architected code
  • Apply Test Driven Development (TDD) and the Red-Green-Refactor cycle with AI assistance
  • Explore how AI can support test creation, refactoring, and design decisions
  • Understand how to maximize productivity while maintaining full control over your codebase
  • Identify common pitfalls of AI-assisted development and learn how to avoid them

It's all about the content.

  • Domain-Driven Design (DDD): helps structure the domain model in a way that AI can understand and reason about, making interactions with AI more context-aware and meaningful
  • Ports and Adapters (Hexagonal Architecture): promotes a clean separation of concerns, enabling AI to safely assist with isolated components without risking tight coupling
  • Modularization: provides clear boundaries and responsibilities in the codebase, allowing AI to generate or refactor code within well-defined contexts
  • Microservices Architecture: enables distributed development and targeted AI assistance on a per-service basis, supporting independent evolution and deployment

Workshop Program

The content of our program can be customised during pre-training analysis.

  1. Fundamentals of Collaboration with Generative AI in a Developer's Daily Work
    1. Introduction to Generative AI in the Context of Software Craftsmanship
      1. What is Generative AI and how it changes the way developers work
      2. Overview of AI tools for Java/Spring developers
      3. Ethics and responsibility when using AI in coding
    2. Basics of Effective Collaboration with AI
      1. Writing effective prompts and communicating with AI
      2. The role of context, intent, and structure in working with AI
      3. Common mistakes and misunderstandings
    3. Practical Exercises: AI as a Partner in Daily Work
      1. Creating business logic code with the help of AI
      2. Code correction and improvement with AI support
      3. Verifying results and the quality of generated code
      4. Implementing technical and integration elements
  2. Quality Assurance – Testing and Architecture
    1. Test-Driven Development (TDD) with AI Involvement
      1. Principles and values of TDD
      2. Red-Green-Refactor with AI assistance
      3. Generating unit and integration tests
      4. Generated tests vs. system correctness
    2. Practical Exercises: AI as an Assistant in Maintaining Quality
      1. Refactoring unreadable code with AI
      2. Extending functionality using tests
      3. Maintainable tests that protect against regression
  3. Architectural Patterns and AI in Complex Systems
    1. Domain-Driven Design and Contextual Understanding by AI
      1. Introduction to DDD and its importance in working with AI
      2. Building domain models with AI readability in mind
      3. Co-creating aggregates, value objects, and domain services with AI
    2. Hexagonal Architecture, Modularity, and Microservices
      1. Benefits of Ports and Adapters in the context of AI
      2. Creating and refactoring modules in large systems
      3. Scaling solutions with AI support at the microservices level
    3. Practical Applications and Implementation Strategy
      1. Case study: extending an existing application with AI
      2. Case study: building new applications with AI
      3. Defining boundaries of responsibility between humans and AI
      4. AI implementation plan for a production team


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Trainers

Meet the experts who will conduct your workshop.

Michał Michaluk

Contact us for a free consultation.

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If you prefer direct contact then you can always call.

Iwona Sobótka

Training coordinator


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