Hi everyone — I’m a Java/Spring Boot engineer with 17+ years of experience, and I’ve spent the past several months building EngineerPrep.
I kept running into the same problem while learning AI engineering: many courses either stay at the conceptual level or demonstrate everything inside notebooks. They explain what tokens, embeddings, RAG and agents are, but not how these pieces behave inside a production application.
So I built the kind of learning path I wanted:
73 focused AI engineering lessons
Short chapters instead of long video lectures
Visual walkthroughs of what happens inside the system
Production incidents and failure scenarios
Hands-on implementation labs
Runnable Maven projects with local Ollama support
Optional OpenAI and Amazon Bedrock configurations
An AI mentor that answers within the context of the current lesson
The curriculum progresses through:
LLM Foundations
Prompt Engineering
Structured Output and Validation
RAG and Embeddings
AI Memory
Agents and Tool Calling
Model Evaluation
AI Security
Production AI Systems
The complete LLM Foundations module is free: 15 lessons, hands-on labs and a starter project that runs locally with Ollama. No paid AI API or credit card is required.
EngineerPrep is intentionally focused: one structured path for working software engineers who want to understand how AI systems are designed, implemented and debugged in production—especially with Java and Spring Boot.
You can try it here:
I would genuinely appreciate feedback on three things:
Does the first lesson make the value clear quickly?
Is the lesson → incident → project structure useful?
What would prevent you from completing the free module?
I built this independently, so direct criticism is welcome. It will help me decide what to improve next.
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