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AI Training

AI Training

AI Training - Practical Applications for Modern Development

This comprehensive training category covers the integration of artificial intelligence tools and techniques into professional software development workflows. Topics include leveraging AI assistants for code generation and debugging, implementing AI-powered testing strategies, using machine learning models in application development, and best practices for prompt engineering. Participants learn to effectively collaborate with AI tools to enhance productivity, improve code quality, and accelerate development cycles while maintaining professional standards. The training emphasizes hands-on experience with current AI platforms, practical implementation strategies, and staying current with rapidly evolving AI capabilities in the software development ecosystem.


This description positions AI training as a practical, professional development offering that would fit well alongside your existing training in Scrum, TDD, and software design patterns. It emphasizes the practical application aspect that seems to align with your teaching philosophy of hands-on, real-world skills.

Generative AI For Scrum Teams
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Generative AI For Scrum Teams

In this dynamic and hands-on class, you'll explore how tools like ChatGPT, GitHub Copilot, and AI-powered planning assistants can help your Scrum Team boost productivity, streamline communication, and foster creativity in product development.

AI-Enhanced Scrum: Transforming Agile Development with Artificial Intelligence

Revolutionize Your Agile Development with AI-Enhanced Scrum

AI-Enhanced Scrum Course Summary

 

Overview

AI-Enhanced Scrum is a comprehensive training program that transforms traditional Agile development by integrating Artificial Intelligence throughout the entire software development lifecycle. This course teaches teams how to leverage AI for enhanced productivity, quality, and consistency from initial requirements gathering through final deployment.

What You'll Learn

Students master a complete AI-driven development workflow starting with interactive PRD (Product Requirements Document) creation through intelligent AI questioning, progressing through advanced UI design generation using sophisticated non-traditional prompting techniques, UML visual modeling, and culminating in comprehensive technical specification generation that synthesizes all project artifacts.

The course covers AI-enhanced Acceptance Test Driven Development (ATDD) and Test Driven Development (TDD) practices, showing how well-crafted tests guide AI development agents to optimal solutions. Students learn to maintain living documentation where PRDs, UI designs, UML models, technical specifications, tests, and code remain perfectly synchronized throughout development.

Key Modules

  • AI-Driven Requirements Gathering: Interactive PRD creation with intelligent AI interviewing
  • Advanced UI Design Generation: Revolutionary prompting techniques for interface design
  • Visual Architecture Modeling: UML diagram generation and refinement
  • Comprehensive Technical Specifications: AI synthesis of all project artifacts into implementation guides
  • AI-Enhanced Testing: ATDD and TDD practices with AI development agents
  • Quality Assurance & Defect Resolution: AI-powered debugging and consistency validation
  • Sprint Management: AI-enhanced planning, tracking, and retrospectives

Target Audience

Perfect for Product Managers, Scrum Masters, UX/UI Designers, Solution Architects, Technical Leads, Development Teams, and Organizations seeking to modernize their Agile practices with AI automation while maintaining high quality and comprehensive documentation.

Instructor Expertise

Led by an industry veteran with decades of software development experience, from early C++ development to cutting-edge AI implementation, with extensive expertise in teaching Scrum, TDD, software design patterns, and system architecture.

Key Benefits

  • Accelerated Development: Faster feature delivery through AI automation
  • Enhanced Quality: Comprehensive testing and documentation consistency
  • Improved Communication: Clear visual models and specifications for all stakeholders
  • Reduced Technical Debt: AI-maintained synchronization across all project artifacts
  • Modern Workflow: Integration of latest AI tools with proven Agile methodologies

Course Outcome

Graduates will transfo

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