course
AI Fundamentals
From AI user to AI-savvy professional. Understand what happens under the hood, learn to recognize capabilities and limitations, and discover how to use AI responsibly in your daily work.
Description
AI has become part of much of our daily work. But what do we actually mean by AI, Machine Learning, Generative AI, and Agentic AI? How does a Large Language Model (LLM) work, and why does it sometimes confidently provide an incorrect answer?
In this training, you will gain a practical and technically grounded foundation in modern AI. You will learn how AI models work, what their capabilities and limitations are, and how to critically evaluate AI output.
We cover subjects such as the mechanics of LLMs, context windows, multimodality, different models, and "thinking effort." We also examine hallucinations, bias, and limitations in training data. You will learn how to craft prompts efficiently and understand the role played by tokens and costs.
Finally, we look at the responsible use of AI. What does the AI Act entail? What privacy and security aspects are involved? And what should you consider when using AI within an organization?
After this training, you will have a solid foundation—not just to use AI, but to better understand it, accurately assess its output, and apply the technology responsibly.
Learning Goals
 Explain the differences between AI, Machine Learning, Deep Learning, Generative AI and Agentic AI. | Understand  |
| Describe how Large Language Models generate responses and explain their key limitations. | Understand |
| Explain the implications of the EU AI Act and responsible AI practices for the use of AI in an organizational context. | Understand |
| Identify hallucinations, bias and other common AI pitfalls in AI-generated output. | Analyze  |
| Evaluate the strengths and weaknesses of different AI models and providers for specific use cases. | Evaluate  |
| Apply effective prompting techniques to improve the quality and efficiency of AI-generated results. | Apply  |
Prior Knowledge
- None
Subjects
- AI taxonomy: AI, Machine Learning, Deep Learning, Generative AI, and Agentic AI
- Philosophical and historical framework: Turing Test, Chinese Room, AGI, and ASI
- How an LLM works: The technical mechanics of Large Language Models
- Context window: Size, limitations, and the bathtub metaphor
- Multimodality: Understanding and generating images, audio, and video, and their workplace applications
- Different models: Providers, backgrounds, model variants, and thinking effort
- Hallucinations: Why a model invents things instead of saying "I don't know"
- Bias & training data limitations: Preconceptions and knowledge cut-off dates
- Critically evaluating AI output: Verification versus blind trust
- Effective and efficient prompting: Tokens, prompting, and costs
- AI Act: What the regulations say
- Privacy, security & AI policy: Responsible use of AI tools within organizations
Schedule
| Start date | Duration | Location | |
|---|---|---|---|
December 15, 2026 | 1 day | Veenendaal / Remote This is a hybrid training and can be followed remotely. More information | Sign up |
All courses can also be conducted within your organization as customized or incompany training.
Our training advisors are happy to help you provide personal advice or find Incompany training within your organization.
"Trainer who knows his profession!"Marc
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