LLMs or ML First?
The Answer Depends on You.
The internet is full of conflicting advice. Here's how to decide
which path actually makes sense for your goals in 2026.
Conflicting Advice Is Keeping You Stuck.
Some say you need years of ML fundamentals. Others say jump straight to LLMs. Both sound convincing.
Fear of missing fundamentals wars with FOMO on the latest tech that's actually getting hired.
You've spent weeks researching instead of learning because you're terrified of picking the wrong path.
Your Goals Determine the Path.
The World-Class AI Engineer Cohort
There's no universal answer. But there is a right answer for you. In 2026, most developers building AI products don't need deep ML theory. They need to ship. Let your end goal guide your starting point.
Define Your Goal
Building products? Research? Different paths.
Match Path to Goal
LLM-first for builders, ML-first for researchers
Execute With Clarity
Stop second-guessing, start shipping
Meet Your Mentor
My aim has been the same for years: become a world-class AI engineer. Every career move I've made has been measured against that.
I started as a software tester on a $500/month internship in the Netherlands. Taught myself to code, learned to ship real systems, and worked my way to Senior Engineer at GitHub.
Then I left GitHub. I joined an AI research lab as Member of Technical Staff, where I currently build products for secure AI monitoring.
The cohort draws directly from my real experience so you can make progress fast.
I run this special cohort with only a few people because hands-on work with me is what it takes to bring you to become a world-class AI engineer.
Real Results
Vittor
AI Engineer
Built and deployed his portfolio piece, then landed the AI role
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
8 Weekly Tuesday Sessions
3 hours each for 24 live hours total.
Project Scoping at Kickoff
We set the scope of what you'll ship and the milestones to get there before the live sessions start.
Code Reviews
Reviews of your code from Zen during the cohort.
Lifetime Demo Access
Every architecture demo is recorded and yours to keep.
Demo Day
You present what you built and get feedback from Zen, with a recording you can use in your portfolio.
12 Months Community Access
Included with the cohort.
Every Week Spent Debating Is a Week Not Learning
Frequently Asked Questions
Is it really okay to learn LLMs without ML background?
Yes, for most practical applications in 2026. If your goal is building AI-powered products, you can be highly effective using LLMs as powerful APIs without understanding backpropagation. Most AI engineers at startups spend their time on prompt engineering, RAG systems, and integrations, not training models from scratch. You can always backfill ML theory later if you hit the ceiling.
When should I learn traditional ML first?
ML-first makes sense if: 1) You want to do AI research or work at research labs, 2) You're interested in training or fine-tuning models from scratch, 3) You want to work on non-language AI (computer vision, robotics, recommendation systems), 4) You're genuinely curious about the math and theory. If none of these apply, LLM-first is likely faster to value.
Can I learn ML fundamentals later if I start with LLMs?
Absolutely. Many successful AI engineers started with high-level tools and backfilled theory as needed. Starting with LLMs gives you quick wins and momentum. When you hit limitations or want deeper understanding, you'll have context for why the theory matters. Learning is not a one-way door.
What does the 2026 job market actually want?
The majority of AI engineering roles in 2026 are about building with LLMs, not training models. Companies want people who can ship: RAG systems, agents, tool use, evaluations, production deployments. Deep ML knowledge is valued but not required for most positions. The bottleneck is practical experience, not theoretical depth.
How much time will this take?
You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.
How can coaching help me decide?
A coaching session cuts through months of analysis paralysis. In one conversation, we map your specific background, goals, and constraints to a clear learning path. No more Reddit debates or YouTube rabbit holes. You walk away knowing exactly what to learn, in what order, and why it's right for you.
I've signed up for cohorts before and dropped out. How is this different?
It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.
I'm not pivoting careers. I want to build a product. Does this still work?
Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.
Do I need prior AI experience?
You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.
What does it cost?
It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.
Can I do this while working full-time?
Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.
I accept those who have the highest chance of success.
In the 30-minute call we discuss your goals and whether you are ready for the program.