Generative AI (GenAI)
Foundational to advanced generative AI — how these models work and how to build real applications on top of them.
One-time course fee
Who should take this?
Developers, data practitioners, technical consultants, and builders moving from prototypes to usable AI applications.
Capability developed
Build, ground, package, deploy, and evaluate AI applications across the modern engineering stack.
Professional application
AI application delivery, technical consulting, product engineering, or forward-deployed work.
What’s inside this track
- 1Foundations
Build: Implement a basic embedding-based similarity search.
Evidence: Understands what's actually happening inside the model, not just the API surface.
- 2Prompting & Structured Outputs
Build: A reliable structured-output pipeline for a real task.
Evidence: Gets consistent, parseable output instead of fighting the model every single call.
- 3RAG & Grounding
Build: A grounded Q&A system over a real document set.
Evidence: Ships a GenAI feature that doesn't make things up.
- 4Fine-Tuning & Deployment
Build: Capstone: fine-tune a small model for a narrow task and deploy it behind an API.
Evidence: Knows when to fine-tune vs. when prompting or RAG is enough — and can execute either.
What you get
What you will own
Not a promise — a concrete list of what you personally build and can show a buyer, pulled from this course’s real curriculum.
- A working project: Implement a basic embedding-based similarity search.
- A working project: A reliable structured-output pipeline for a real task.
- A working project: A grounded Q&A system over a real document set.
- A working project: Capstone: fine-tune a small model for a narrow task and deploy it behind an API.
- A completed capstone: Enterprise Knowledge Assistant Capstone, scored across multiple dimensions
- A portfolio entry documenting how you built it, not just that you finished
- Verified skill evidence — tied to real work, not a self-rating
What you’ll prove — and own — by the end
Completion isn’t the finish line. This is what turns the course into evidence you can actually use.
Personalised from day one
A short entry diagnostic flags which modules to move through quickly and which to slow down on — the full curriculum stays available either way.
Evidence, not checkmarks
Skills track through a real mastery ladder — module completed, exercise practiced, project shipped — not just “lesson viewed.”
Your Final real-world project, AI-defended
Enterprise Knowledge Assistant Capstone — submit real work, defend your decisions to an AI interviewer, get scored feedback across multiple dimensions.
A portfolio you can use today
Turn a finished project into a case study, resume bullets, and an interview story — drafted from your real work, yours to review before you use it anywhere.
Connect this course to practical work
See how Ropes links a skill to a business problem, portfolio proof, a bounded service, and responsible client delivery. No client or income outcome is guaranteed.
Earnings potential
A simple illustration of how freelancers commonly structure retainer pricing once they’re taking on client work.
AI feature consulting
Illustrative example — your results will vary based on niche, effort, and client base.
Industry snapshot
Real, sourced research — current tools, market signal, and named case studies, each with a citation. Not marketing copy.