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 precisio