The AI Engineer Onramp: From Vibe Coding to AI-Assisted Development
"Dr. Greg" Loughnane, Ph.D.
Co-Founder & CEO @ AI Makerspace | #unautomatable | TEDx Speaker
Kat "KatGPT" Gawthorpe, Ph.D.
Co-Founder @ eve.ai | Econometrics @ Fannie Mae
Build and evaluate your first LLM app - like a proper AI Engineer
We built this course after working with hundreds of students over the past two years in The AI Engineering Bootcamp.
Why?
Because even though students could complete The AI Engineering Challenge, that didn't mean that they really understood the core concepts and code required to build 🏗️, ship 🚢, and share 🚀 end-to-end LLM applications.
You know, the important buy kind of boring bits.
How do you actually construct a front end using tools from the React Ecosystem (e.g., Next.js, Remix, etc.)?
What about a back end using Python (e.g., FastAPI, Flask, Django, etc.)?
What about connecting them using AI-assist with industry-standard version control and branching?
🎧 Vibes, obviously, right?
When things go bad, you'll be able to revert back?
When you need to add a feature - like PDF upload for RAG or basic Agentic RAG - just more vibes?
How often does one-shotting with simple prompts, no rules or external connections actually work out?
It turns out, not as often as the world would have you believe.
You still have to do engineering to be an AI Engineer.
Take the time you need to learn the boring bits before going all in for 10 weeks on The AI Engineering Bootcamp.
Welcome to the Onramp.
What you’ll learn
Build 🏗️, ship 🚢, and share 🚀 an end-to-end LLM application with AI-assisted development, vibe-coding, vibe-checking, and leading tools.
Learn the ropes of the best-practice AI Code Editor on the market
Set up your AI Interactive Development Environment like a pro
Grok the difference between vibe-coding and AI-assisted development
Learn Cmd+L, Cmd+K, how to use Cursor CLI
Develop your own style of vibe checking
Do simple quantitative evaluations on basic LLM wrapper apps
Finally commit to learning Git commands
Start your GitHub, using AI-assist to help you get there
Who this course is for
Aspiring AI Engineers who do not yet code every day, but know they want to code every day.
Aspiring AI Engineering Leaders who lead coders and want skin in the game on building, evaluating, and deploying LLM applications.
Potential Aspiring AI Engineers who do not yet code every day, but think they might want to code every day.
What's included
Live sessions
Learn directly from "Dr. Greg" Loughnane, Ph.D. & Kat "KatGPT" Gawthorpe, Ph.D. in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Featured lesson
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Context Engineering for Agents & Multi-Agent Systems
Context Engineering is the new...
Not just prompt engineering, but also RAG! Learn why this fundamentally makes intuitive sense for Generative AI
How agents with tools help us to enhance context
Enhancing search and retrieval is the primary job of agents with access to tools (and MCP servers!) today
How multi-agent systems enhance context
How tools like Deep Research offer insights into using parallelization to further scale context optimization
Schedule
Live sessions
1-10 hrs / week
We typically have 10+ hours per week of office hours that you can attend (1 for each instructor and peer supporter!)
Weekly Programming Projects
1-3 hrs / week
Each class period, we will get hands-on with Python coding homework!
Async content
The AI Engineer Challenge
In this repository, we'll walk you through the steps to create a LLM (Large Language Model) powered application with a vibe-coded frontend!
Are you ready? Let's get started!
🖥️ Accessing "gpt-4.1-mini" (ChatGPT) like a developer
🏗️ Forking & Cloning This Repository
⚙️ Backend Setup with uv
🔥Setting Up for Vibe Coding Success
😎 Vibe Coding a Front End for the FastAPI Backend
🚀 Deploying Your First LLM-powered Application with Vercel
Open-Source Onramp from The AI Engineering Bootcamp, Cohort 7 (June-Aug, 2025)
This repo contains the AIE7 Onramp materials — a 6-week live session series originally run as preparation for the AI Engineering Bootcamp (Cohort 7).
We’ve open-sourced these sessions so anyone can use them to build a strong foundation in AI engineering concepts, tools, and practices.
Github link
*Note, we no longer teach LLM Training, Fine-Tuning, or alignment - for that material check out the resource below below!
Open-Source LLM Engineering Course (2024)
Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment.
Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.
Course ModulesThis course teaches you the fundamentals of LLMs, and will quickly onramp you up to the practical LLM Engineering edge. When you complete this course, you will understand how the latest Large and Small Language Models are built, and you'll be ready to build, ship, and share your very own.
Module 1: Transformer: Attention Is All You Need🤖 The Transformer
🧐 Attention
🔠 Embeddings
🪙 Next-Token Prediction
🔡 Embedding Models
🚇 Pretraining
🚉 Fine-Tuning
🛤️ Alignment
🥪 Model Merging
⚗️ Distillation
Frequently asked questions
$500
USD
2 cohorts