Whether you write the code, test it, plan it or analyse it, learn to direct AI with clear intent, build work that holds up, and prove the result is right.
Find the course for your role ↓Built around real delivery work: weekly sessions, worked examples and assignments reviewed against clear criteria.
Use AI models across the development cycle, from understanding a repository to reviewing a tested, maintainable change.
Certificate of completionEarn it when you passTranslate a rough idea into an executable specification that guides an AI agent from implementation to verified behavior.
Certificate of completionEarn it when you passDesign, build, and evaluate AI agents that use tools, handle uncertainty, and know when to ask a human.
Use AI to sharpen discovery and decisions, write briefs that teams and coding agents can build from, and ship AI features with clear outcomes, guardrails and evidence.
Certificate of completionEarn it when you passUse AI to elicit, structure and validate requirements, from stakeholder conversations to acceptance criteria, data rules and change impact that developers, testers and AI agents can rely on.
Use AI to design tests from requirements, automate them responsibly and catch the failures AI-generated code tends to hide, with evidence your team can trust.
Certificate of completionEarn it when you passDesign evaluations, guardrails and monitoring for language-model and agent systems, and turn them into evidence that product, engineering and compliance can act on.
Define the problem, the boundaries and what success looks like. A model cannot make those decisions for you.
Apply each idea to realistic work, such as a specification, a test plan, a backlog or a change request, and leave with material you can reuse.
Review the output. Test the assumptions. Keep the evidence that tells you what actually works.
