Applied Practice — Free AI Learning Track

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

• AI Coding Workflow 101 — AI makes code faster only for teams with a disciplined workflow. Everyone else ends up debugging confident mistakes.

Part of the free AI Learning Hub by Shahzad Asghar.