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Applied Artificial Intelligence – A Structured Curriculum for Business Decision-Makers

To deploy and use AI or make informed strategic decisions it is fundamental to have a basic understanding about the foundations and inner workings of the AI Black Box.

Start by building a mental model of BEFORE - INSIDE - AFTER the Black Box, this is a big deal already to start looking behind the scenes.

Dentro de AI translates to Inside AI and you have come to the right place to gain basic insights without the need for an PhD. I am on the same curious journey already for some years and here you find what I collected so far.

BEFORE → INSIDE → AFTER

Why Learn AI Now

  • Understand the Black Box: move from magic to mental models.
  • You press a button and get a result - but what is really happening in AI under the hood to produce this?
  • Improve results: tune your skills and level up your game.
  • Spot real use cases: with experience you make better decisions where to use AI - and where it might even harm.
  • Manage risk: privacy, bias, hallucinations, prompt injection.
  • Future-proof your career: this is just the beginning.

If AI feels overwhelming, that’s normal. The ecosystem moves fast, the math can be heavy, and the marketing is loud. The way through is a clear mental model and some guideance for navigation.

The Basics: AI Timeline & AI Glossary

How did we get here?

Explore the AI Timeline with the big influencial events listed in chronological order.

What are all these crazy terms?

The Glossary has you covered - including a quiz to validate your knowledge.

Mental Model: BEFORE → INSIDE → AFTER the AI Black Box

Think in three phases: before, inside, and after the black box. Many of the concepts described here are listed with details and interaction in the AI Visualizations section.

BEFORE - IN - AFTER

BEFORE the AI Black Box

  • Tokenize: text → tokens (results in Vocabulary).
  • Architecture: Transformer with layers; parameters are learned.
    • Open Weights vs Closed Weights
    • On Prem vs Cloud
    • Text, vision, multimodal
  • Embeddings: tokens → vectors that capture meaning.
  • Model type: text-only, vision, or multimodal; open vs. closed weights; on‑prem vs. cloud.
  • Training data + objectives, watch out for bias in training data.

INSIDE the AI Black Box

  • Giant Mathematical Function
    • hundreds of layers and billions of Parameters - learned during large scale training
    • high-dimensional space
    • Non-deterministic by design
  • Probabilities: predicts the next token distribution, not “truth”.
  • Attention: each token looks at others to decide what matters.
  • Sampling: temperature & top_p control creativity vs. determinism.

AFTER the AI Black Box

  • Context window: how much the model can “hold in mind”.
  • Prompt patterns: role, task, context, constraints, output template.
  • Tools: function calls, retrieval (RAG), code execution, image tools.
  • Guardrails: safety filters, validation, traceability and evaluation.

This mental model allows you to visualize structure and flow - and allows you to improve your knowledge in areas that need improving.

Dive deeper: Dedicated Articles & Interactive Visualizations

Explore these sections in dentro.de/ai to gain insights on dedicated subjects that accomplish the Learning Curriculum.

Articles in the AI Blog section

  • How Large Language Models like ChatGPT Work
  • AI Model Lifecycle - Analogy Explained with Cars
  • Analogy to understand Open Source in generative AI Models

Interactive AI/ML Visualizations and Videos

Interactive Websites

  • Tiktokenizer
  • Moebio Mind
  • Transformer Explainer
  • BertViz
  • LLM Visualization
  • LLM Architectures

Videos

  • Large Language Models explained briefly
  • Deep Dive into LLMs like ChatGPT

Fun Challenge

  • Bouncing Ball Hexagon
  • Vitruvian Robot