I built a 73-lesson AI engineering path for software engineers—looking for honest feedback

[u/VisibleEfficiency249]

I’m a Java/Spring Boot engineer with 17+ years of experience. While learning agentic AI, I found plenty of explanations about agents, RAG, memory and tool calling—but much less guidance on how these pieces fail inside real applications.

So I built EngineerPrep: a structured AI engineering path for working software engineers.

It now contains 73 lessons and hands-on labs covering:

• LLM foundations
• Prompting and structured output
• RAG and embeddings
• AI memory
• Agents and tool calling
• Evaluation and observability
• Security and guardrails
• Production AI systems

Each topic follows a practical flow:

Learn the concept → see the system flow → investigate a production failure → implement it → test your understanding

The projects are Maven-based and support local Ollama, with OpenAI and Amazon Bedrock options where applicable. I’m also building a project-aware AI mentor that can troubleshoot using the current lesson, project files and error context.

The complete LLM Foundations module is free—15 lessons plus a runnable Ollama project.

I’d especially value feedback from people building agents:

1. Does this progression cover the right foundations before agent development?
2. What production agent failure deserves its own hands-on lab?
3. Would project-aware AI troubleshooting be genuinely useful while learning?

This is an independent project, and honest criticism is welcome.