RAG Is Simpler Than You Think: A Practical Guide

The article argues that many RAG stacks are over-engineered, recommending starting with full-text search (BM25, Elasticsearch) and only adding embeddings, vector DBs, and reranking when data proves the need. It provides decision factors like data freshness, corpus churn, query patterns, scale, and team capabilities.