Full Code Repos
Next.js + Vercel AI SDK SaaS starter, LiveKit voice RAG bot, and more production AI app repos dropping over time.
View example repoAI engineering career community
Become a
Real AI Engineer
with Johannes Hayer
Engineering fundamentals are your biggest career advantage.
I went from Cloud Architect to landing a high-paying AI engineering role. I am building this Skool community to help developers make the same transition with production code, structured learning, and direct feedback.
AI changed the tools, not the standard. Coding agents can ship code quickly, but they still need engineers who understand architecture, feedback loops, evaluation, data flow, and how to keep systems maintainable.
The opportunity is not to become a prompt collector. It is to become the engineer who can design, build, and explain reliable AI systems in production.
Production AI apps
RAG and retrieval systems
MCP and agent workflows
Context engineering
Evaluation-first development
Voice AI systems
Public proof
Start with the mini course, then go deeper in Skool.
MCP, RAG, agents, voice apps, context engineering, evals, and production AI systems are the skills that separate AI engineers from tutorial followers.
The public course gives you the mental model. The community adds the repos, learning system, calls, and 1:1 feedback to turn that model into shipped work.
Join communityWhat you get inside
A practical system for becoming employable in AI engineering.
AI in a Shell
A structured learning app with micro-courses, active recall, and a Socratic AI tutor for daily AI engineering practice.
Knowledge Graph
A growing map of AI engineering concepts so you can connect models, RAG, agents, evals, and production architecture.
Code, Calls, and 1:1
Weekly engineering calls, code feedback, and monthly 15-30 minute personal sessions to help you move faster.
Clone real systems
Work from production-oriented repos instead of short demo apps that fall apart when requirements change.
Build the mental model
Learn when to use prompting, RAG, finetuning, agents, evals, and guardrails in the same architecture.
Get direct feedback
Use live calls and 1:1 sessions to improve your work, portfolio, and career transition plan.
Free mini course
Start with the mental model.
Read the course on-site, then use the Skool community to go deeper with code, feedback, and a structured path.
01
The AI Engineering Mental Model
What AI engineering actually is, how it differs from ML engineering and software engineering, the three-layer AI stack, and the most important mindset shift: LLMs are not functions.
02
Models as Probabilistic Infrastructure
What post-training (RLHF, DPO) actually does to model behavior. How sampling and temperature work. Structured outputs. Why the same prompt returns different answers — and what that means for how you build.
03
Evaluation First
Why evaluation is the hardest problem in AI engineering. Three methods: exact match, AI-as-judge, and comparative evaluation. How to build an eval pipeline. How to select a model using your own data — not public benchmarks.
04
Prompt and Context Engineering
System vs user prompt architecture. In-context learning: zero-shot vs few-shot. The context window as a constrained resource, not free space. Defensive prompt engineering: injection, jailbreaking, and information extraction.
05
RAG vs Finetuning — The Decision Framework
When to use RAG, finetuning, or just prompting. RAG architecture and what makes retrieval fail. Why most finetunes don't work — and the three conditions that justify trying. PEFT and LoRA as the practical entry point.
06
Production AI Architecture
The production pipeline: input guardrails → context enhancement → model routing → generation → output validation → caching. Monitoring, observability, and how to close the user feedback loop.
Build the skills that make you credible as an AI engineer.
Join the Skool community for the learning system, code repos, calls, feedback, and 1:1 guidance.
Join community