RAG Is Simpler Than You Think

Most organizations over-engineer their RAG systems by immediately adopting embeddings and vector databases, when simpler approaches like full-text search often suffice for common use cases. The article presents a decision framework based on data freshness, corpus characteristics, query patterns, scale, and team capabilities, recommending that teams start with basic full-text search (BM25) and only move to more complex solutions when data proves it's necessary. Full-text search handles keyword-based queries effectively, requires no ML expertise, and eliminates chunking and model deprecation concerns that plague more sophisticated approaches.