What Most RAG Tutorials Don't Teach You

LOI CHIANG HAO — Sat, 06 Jun 2026 09:26:55 +0000

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?

DEV Community: LOI CHIANG HAO

What Most RAG Tutorials Don't Teach You

Why I Built AI Model Atlas

Discussion