Track 1
Foundations
What these systems are and how they learn.
The Foundations track explains what generative AI systems are and how they learn. It is the shared vocabulary layer for everything else on this site: if you can describe tokens, parameters, context windows, and reward signals precisely, you can govern the systems built on them.
Four modules build that vocabulary in sequence. Generative AI 101 covers the two core architectures, autoregressive and diffusion, and the full system around the model, from data pipelines to serving metrics. AI Concepts 101 frames the shift to Software 3.0, where natural language becomes the programming interface and evaluations replace traditional tests. The LLM Concepts module works through tokens, embeddings, the three training phases, prompting techniques, and the five core failure modes. Reinforcement Learning 101 explains the agent-environment loop and the human feedback process that aligns modern models.
Read the modules in order if you are new to the field. Each one is a short, plain-language briefing aimed at practitioners and decision-makers, and each ends with the questions a leader should ask and the failures to watch for.
Modules in this track (4)
Generative AI 101
Every design decision about a GenAI product starts with what kind of model it needs and what the model actually does under the hood.
AI Concepts 101
Software engineering has split into three eras, and leaders need vocabulary to steer teams through the current one.
LLM Concepts, A Deep Dive
The vocabulary for discussing language models is the vocabulary for governing them.
Reinforcement Learning 101
Reinforcement learning now sits behind the models and agents senior leaders are asked to approve.
How this track fits
The AI Learning Hub is built as a sequence rather than a library. Foundations covers what the models are and how they behave. Context covers the material a model needs around it before its answers can be trusted. Practice covers what changes when a system leaves a demonstration and meets real users, real data, and a real obligation to be right.
Tracks can be read in any order, but the sequence matters more than it looks. Most failures in institutional AI are not model failures. They come from deploying a capable model into an environment that was never prepared for it: no owner, no stopping condition, no measure agreed in advance. The later tracks exist because the earlier ones are not sufficient on their own.
For the foundations underneath all of this, the free Learn LLMs course works from next-token prediction up to the Transformer, and LLM Architecture Explained covers the same ground visually. To move from models to systems that act, see Learn Agentic AI. For the governance that has to sit around any of it, see AI governance in the United Nations and the governance templates.