From Naive to Agentic: A Developer's Guide to RAG Architectures

If you've built even one LLM application, you've likely encountered the hallucination problem. Your model sounds confident but makes things up. Or worse, it knows nothing about your company's private data because its training cutoff was two years ago. Enter RAG (Retrieval-Augmented Generation). RAG is the standard pattern for connecting LLMs to external knowledge. But here's the catch: Not all RAG pipelines are created equal. A simple "retrieve-and-read" setup might work for a demo, but it will fail in production. In this article, we'll break down the 4 main types of RAG architectures, what specific problems they solve, and how to choose the right one for your use case. 🧱 1. Naive RAG (The "Hello World") This is the baseline implementation you see in most tutorials. The Flow: User Query → Vector Search → Top K Chunks → LLM → Answer The Problem It Solves (and Creates) Solves: Basic knowledge grounding. It stops the model from relying solely on parametric memory. Creates: Low precision