The engineering take on AI — context, RAG, agents, serving, and the production lifecycle.
The engineering path through applied AI. Manage the context window as a resource, build retrieval and production agents, then scale, serve, and operate them. Covers context engineering, RAG, production agents, inference and serving internals, enterprise serving stacks, and the full MLOps/LLMOps lifecycle. Python-first with runnable code, and three labelled prompts per task so your AI teaches the mechanism instead of hand-waving it.
The demo works in an afternoon. Making it right often enough that people stop double-checking it is the actual job.
Most agent demos fall apart the first week they meet real traffic, real attackers, and a real bill. Build one that doesn't.
The demo that delighted ten people falls over — or bills $30K a month — at ten thousand. Learn each move before you need it.
A demo that answers instantly for one user can crawl — and bill a fortune — once real traffic arrives. Fix that before it ships.
One Python process per model works fine until you have thirty models, shared GPUs, and a latency promise you have to keep.
Demos break quietly once real traffic hits them — learn the versioning, testing, monitoring, and rollback habits that keep them reliable.
Your prompt is a sliver of what the model actually reads — the rest decides whether the answer is right, fast, and affordable.
Your agent works for ten turns, then forgets the goal, leaks a secret, or bills $20K a month. This is the layer that stops all three.
Reading about applied AI doesn't get you hired or shipped — ten complete builds, each with real evals, real costs, real deployment.
Hours a week vanish into mail only you can answer. Build the bot that answers it instead — and can be stopped mid-send.
Shipping on vibes means every prompt tweak is a coin flip — build the datasets, scorers, and CI gates that catch damage before users do.
The gap between a voice demo and a voice product is measured in milliseconds and interruptions. This course closes it.