What Most RAG Tutorials Don't Teach You

Most RAG tutorials stop at something like:

Vector Search → LLM → Done

And for learning the basics, that's completely fine.

The problem is that once systems become larger, several additional layers start to matter:

• Query routing
• Hybrid retrieval
• Semantic caching
• Evaluation and feedback loops
• Failure handling and fallback logic

These topics are often mentioned briefly, if at all, in beginner tutorials, yet they can have a significant impact on cost, reliability, and user experience.

Why I Built AI Model Atlas

I wanted a way to study and visualize these architectural patterns without turning them into another framework.

So I built AI Model Atlas, a learning-focused repository that explores concepts such as routing, hybrid retrieval, caching, evaluation, and execution control through guided modules and runnable examples.

GitHub:
https://github.com/Hao610/AI-Model-Atlas

The goal isn't deployment.

The goal is understanding how production-style AI systems are structured and why the simple tutorial version often isn't enough.

It is designed as:

• A learning-focused architecture simulator
• A guided curriculum (36 modules)
• A reference architecture for RAG system design
• A conceptual bridge between tutorial systems and production thinking

Discussion

I'm curious:

What layers have you found most important when moving a RAG system from demo to production?

• Routing?
• Evaluation?
• Caching?
• Observability?
• Something else?