Build with language models, in earnest.
Beyond the demo. This track is about shipping generative-AI products that are useful, evaluated, and honest about their limits — retrieval, agents, guardrails, and the judgment to deploy them.
Who it's for
Built for these people.
Developers integrating LLMs into real products.
Founders building AI features that have to work.
Engineers tired of prompt-and-pray.
Technical PMs who need to go a level deeper.
What you'll be able to do
By the end.
Design and ship a retrieval-augmented application.
Build agents that use tools reliably.
Evaluate model output instead of guessing.
Put guardrails around what a model can and can't do.
The Curriculum
From how models work to a product you shipped.
A structured sequence — each module builds on the last, with assessments along the way.
How language models actually work
Tokens, context, and sampling — and the failure modes you'll spend your career managing.
Prompting as engineering
Structured prompting and few-shot patterns — and why “just ask nicely” doesn't scale.
Retrieval-augmented generation
Grounding models in your own data: embeddings, vector search, and RAG that holds up.
Agents & tool use
Giving models the ability to act — and the discipline to do it safely.
Evaluation & guardrails
Building eval harnesses and safety rails so quality is measured, not hoped for.
Capstone
Ship a working generative-AI application, evaluated end-to-end.
How you're certified
Earned, not awarded for attendance.
Enrol
Join a cohort or start self-paced, placed at the right level for your background.
Learn
Work through the modules with short assessments at each stage.
Build
Complete the capstone — real work, reviewed against a published rubric.
Certify
Earn a GIAI Certificate tied to what you actually produced.
Admissions
Request your place.
We'll send the full syllabus for this track and the next intake dates.