Build Real Systems with AI
Build the whole solution lifecycle
From a problem to a system you can explain
The reviewed first exercise starts with four parts: users, data, rules and screens. Map a familiar system, explain the rules and write a short brief before asking AI to build. Later track topics cover business applications, AI apps and shipping considerations.
- Clarify the user’s need and scope.
- Define data, rules, roles and permissions.
- Build a small prototype with fictional data.
- Test failures, privacy and usability.
- Agree hosting, ownership and maintenance before deployment.
The sequence above is a suggested project journey, not a claim of automatic production readiness.
What a System Actually Is → · A service-team learning example →
What this direction develops
Think before you prompt, design the data, then build the systems local businesses pay for: e-commerce, websites, booking, POS, ERP-lite, AI apps, and ad tools — with security, pricing in PKR, and handover.
AI supports the work; it does not grant a professional licence, remove regulation or replace accountable judgement. Choose the track because its tasks match your goal, not because its title sounds advanced.
Topics listed in its public detail guide
Audience described by the guide
Vibe-coders and opencode users who prompt AI but need to learn to DESIGN systems
Career directions to explore—not placement promises
Read the actual track requirements →
Directory and detail pages sometimes differ in module counts or scope. This list reflects the reviewed detail page; check the current platform before enrolment.
A worked practice brief
Illustrative situation
A shop needs a booking and stock system, not a pile of generated code.
Discover the workflow, design data, prototype, test permissions and prepare a pilot handover.
Before using a tool, identify the user, the intended output, the source information and what must stay private. Use fictional or explicitly approved information while learning.
Practice the conversation
Explain the system to a non-technical owner.
Try your own response, ask a follow-up and examine the trade-off. If your first attempt is vague, improve it with a specific question, a realistic promise and a documented next action. Role-play is rehearsal, not an outcome guarantee.
Show what you can do
A tested prototype, acceptance checklist and maintenance plan.
Include the problem, assumptions, checks, your own contribution and limitations. A reviewed sample is more informative than saying “I know AI”. Use feedback to improve the work and identify the next gap to practice.
Connect the next step
Explore the actual track and its published modules. Combine relevant foundation work with character, English, role-play and appropriate project review. Then choose a bounded next action: a better work sample, a mentor question, an honest application or a small business test.
Explore the learning platform: Current track catalogue. Examples here explain possible learning applications; they are not promises of individual results.
Explore the demo before choosing your next step.
From repeated problem to a tested prototype
A team repeatedly answers the same service questions and wants an AI assistant.
Research → design → build → testFollow the example →
Test the problem before building the app
A founder has an exciting app idea but has not checked whether people face the problem often enough.
Customer discovery + prototype thinkingFollow the example →Questions to connect this topic with real situations
These related, illustrative practice guides can help you rehearse the conversation or task around your learning. Choose a suitable level; they do not replace the actual lesson brief or specialist review.
All topic questions and scenarios · Choose a practice format