Over 80% of enterprise generative AI projects stall in prototype phase. Why? Because naive vector search with cosine similarity works great in a Jupyter notebook with 50 documents, but completely collapses when scaled across enterprise wikis, contractual PDFs, and real customer queries.

In this technical breakdown, we examine the production gap and how to architect industrial-grade Hybrid Retrieval-Augmented Generation (Hybrid RAG) , Reciprocal Rank Fusion (RRF) , and automated evaluation frameworks (Evals) .

1. Why Naive Vector Search Fails in Production

Standard vector databases calculate cosine similarity over dense vector embeddings.…