RAG Is Simpler Than You Think: Start with BM25

Most teams over-engineer their RAG stacks with embeddings and vector databases when a simple full-text search might suffice. This article breaks down six retrieval architectures, from plain BM25 to agentic decomposition, and provides a decision framework based on data freshness, corpus size, query patterns, and team expertise. It emphasizes starting with the simplest approach and only adding complexity when data proves it's necessary, with concrete cost and latency comparisons.