Monarc Made University
A structured curriculum that takes you from zero to production-ready AI engineering. No fluff, no hype — just the systems thinking, tools, and hands-on reps that close the gap between 'I've heard of it' and 'I shipped it.'
There's no shortage of tutorials that show you how to paste an API key and get a chatbot running. That's not enough. This curriculum is built around the mental models, architecture decisions, and real-world constraints you'll face when AI moves from experiment to production.
Project-based learning
Every module ends with a real build, not a quiz.
Iterative curriculum
Updated as the field moves — not frozen at a release date.
Community access
Private cohort where you can share work and get feedback.
Written SOW & clear expectations
You'll know exactly what you're getting before you enroll.
A sequenced path from fundamentals to production — each module builds on the last and ends with something real you shipped.
How large language models actually work — tokenization, embeddings, attention, and inference. Enough theory to debug what breaks in practice.
Systematic approaches to writing prompts that hold up under variation. Few-shot, chain-of-thought, tool use, and structured outputs.
Integrating Claude, OpenAI, and open-source models into real applications — auth, rate limits, streaming, cost control, and error handling.
Retrieval-augmented generation from scratch — embeddings, vector databases, chunking strategies, and hybrid search.
Designing agents that can reason across steps, call external tools, and recover from errors without going off the rails.
Taking AI features from notebook to shipped — observability, evals, latency optimization, and responsible deployment.
You don't need a CS degree. You need to write code, care about quality, and want to understand AI at the level that actually helps you ship.
You can ship software but haven't worked with LLMs. This gets you to production-ready faster than any bootcamp.
You know the backend. This fills in the model mechanics, tooling, and evaluation practices the job actually requires.
Enough depth to make sound architectural decisions — and to hold your engineering team accountable to real quality bars.
Coming from data, design, or product? This gives you the engineering foundation to contribute to AI teams immediately.
Waitlist
The first cohort will be small. Join the waitlist and get early access, the full syllabus, and a straight answer on pricing before anything goes public.
The curriculum is built on real production AI experience. Browse the case studies to see the kind of problems the practice actually solves.