AI Fundamentals for IT Professionals

At the end of the course, you will earn a Certificate of Completion from Spirit in Projects.

Are you a visionary thinker, ready for innovation, and want to know how the use of AI will be most beneficial for your company or your tasks? If you want to evaluate everything related to the field of AI application and engage with best practices, then look forward to this training. Together we will examine current application areas of Artificial Intelligence, Large Language Models, and modern Prompt Engineering. You will not only learn the fundamentals but also how to effectively deploy AI tools in the enterprise context and optimally prepare for AI integration in your company.

Objectives

  • Understand differences between artificial and natural intelligence
  • Discover and evaluate innovative AI application areas
  • Overview of various AI techniques and methods, especially Large Language Models
  • Understand practical AI applications in enterprises and assess impacts on business processes
  • Understand relevant methods for effective AI implementation
  • Master Prompt Engineering techniques and apply them in practice
  • Gain hands-on experience with current LLMs and AI tools

Target groups:

Business Analyst, Requirements Engineer, Usability Expert, Scrum Master, AI Expert, Project Manager, Project Lead, Demand Manager, Portfolio Manager, IT Project Manager, Test Manager, Tester, Test Automation Specialist, Test Engineer, Enterprise Architect, System Architect, Software Architect, Software Designer, Software Developer, Product Owner and anyone who wants to engage with artificial intelligence.

Show dates and times

Syllabus

1. Fundamentals

  • AI vs. natural intelligence
  • Benefits of using AI
  • Weak and Strong AI
  • Difference between AI, Machine Learning and Data Science
  • Problems & limitations
  • When does AI investment pay off?
  • ROI consideration and economic analysis

2. Fundamental AI Application Areas

  • Natural Language Processing & speech recognition
  • Image recognition & facial recognition
  • Intelligent systems and autonomous systems & robots
  • Expert systems
  • Generative AI and its application fields

3. Fundamental AI Implementation Methods

  • Machine Learning
  • Deep Learning
  • Statistics
  • Logic & planning
  • Large Language Models (LLMs)
  • Transformer architecture
  • Multimodal models

4. Large Language Models in Practice

  • Overview of current LLMs (ChatGPT, Claude, Gemini, etc.)
  • Open Source LLMs (Llama, DeepSeek, etc.)
  • Multimodal capabilities: Text, Vision, Audio
  • Use cases in enterprise context
  • Retrieval-Augmented Generation (RAG)
  • Hallucinations and their prevention
  • Security and data protection in LLM usage

5. Prompt Engineering – Fundamentals and Best Practices

  • What is Prompt Engineering and why is it important?
  • Zero-Shot Prompting for simple tasks
  • Few-Shot Prompting for domain-specific applications
  • Chain-of-Thought (CoT) Prompting for complex problems
  • Structured prompts and templates
  • System prompts vs. user prompts
  • Cost optimization through efficient prompts
  • Practical exercise: Prompt optimization for various use cases

6. Application Areas

  • Customer Support and chatbots
  • Content Creation and Marketing
  • Software Development with AI copilots
  • Predictive Analytics and Forecasting
  • Transportation & autonomous vehicles
  • Medicine and Healthcare
  • Finance and Fraud Detection
  • Supply Chain Optimization

7. AI and Society

  • Legal foundations and EU AI Act
  • Ethics
  • Data protection and GDPR
  • Bias and fairness
  • Sustainability and energy consumption

8. AI Tools and Platforms 2026

  • OpenAI (ChatGPT)
  • Anthropic Claude family
  • Google Gemini Pro/Flash
  • Microsoft Copilot with multi-model integration
  • Google TensorFlow
  • PyTorch
  • Microsoft Azure AI Services
  • Amazon AWS AI Services
  • Cloud platforms and their advantages and disadvantages
  • Programming languages for AI development

9. Hands-on Workshop

  • Practical work with various LLMs (Open Source and Closed Source)
  • Prompt Engineering for real business scenarios
  • Evaluation of different LLM models (Small Language Models vs Large Language Models)
  • Use case development for your company
  • Best practices and lessons learned

Advising after course completion

Spirit in Projects