From Naive to Agentic: The Complete RAG Evolution in 21 Patterns

Retrieval Augmented Generation(RAG) Patterns The Evolution of RAG: 21 Patterns from Prototype to Production Retrieval-Augmented Generation (RAG) started simple. Chunk your docs. Embed them. Retrieve the top-k. Stuff it in a prompt. That worked. Until it didn't. Until your retrieval missed context that lived three chunks away. Until your LLM hallucinated over perfectly good documents. Until your users asked questions that required reasoning, not just lookup. New patterns emerged to fix the failures of the ones before them: Query rewriting. Reranking. Hypothetical document embeddings. Graph-based retrieval. Self-RAG. Corrective RAG. Agentic loops that decide whether to retrieve at all. Each one solves something real, and each introduces tradeoffs worth understanding. This guide walks through the complete evolution. Every pattern. What it solves. When to reach for it. And most importantly, why you probably need more than one of them. 21 patterns. One throughline: the relentless pursuit of