Track 4
Applied Practice
How to deliver with AI in real engineering work.
The Applied Practice track turns the concepts from the earlier tracks into daily engineering work. Understanding models, context, and agents is necessary but not sufficient; the difference between teams that gain speed with AI and teams that accumulate confident mistakes is workflow discipline.
The current module, AI Coding Workflow 101, describes that discipline as a loop: brief the model with curated context, agree on a plan before any code is written, generate small reviewable steps, review with both human and AI-assisted tools, write tests alongside the change, and demand an explanation before accepting a fix. It also covers the recovery moves, such as starting a fresh session after repeated failed attempts, and the boundaries that stay human: architecture, cryptographic logic, and data migrations.
The track is deliberately tool-agnostic. Assistants and models change every quarter, but the method survives each generation. If you lead an engineering team, this track gives you the policy questions to ask; if you write code, it gives you a repeatable loop you can adopt this week.
Modules in this track (1)
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.