Information Retrieval / RAG best practices Market: RAG's Forgotten Foundation: Study Information Retrieval

The article argues that Retrieval-Augmented Generation (RAG) is essentially a classic search engine with an LLM appended, and that modern RAG projects fail when teams rely solely on vector embeddings and expensive infrastructure. It recommends re-learning Information Retrieval (IR) fundamentals—lexical search (BM25), hybrid search, multi-stage retrieval pipelines (cheap retrievers + expensive re-rankers), and rigorous IR evaluation metrics (Precision@K, Recall, NDCG)—to reduce cost, improve robustness to embedding model drift, and scale to large data volumes. The author points readers to the textbook Introduction to Information Retrieval (Manning, Raghavan, Schütze) as a practical source of foundational techniques that can make production RAG systems more reliable and affordable.