Why Naive Cosine Similarity Fails in Production RAG

In modern production applications, relying solely on naive cosine distance between query embeddings and raw text chunks introduces severe precision degradations. Documents sharing conversational vocabulary can rank higher than documents with the exact empirical figures needed by the user.

To solve this in clinical and legal production platforms, we architect hybrid retrieval: combining full-text inverted indexes (BM25) with dense embedding representations in pgvector. By applying Reciprocal Rank Fusion (RRF) and re-ranking candidates through a cross-encoder model, retrieval accuracy jumped from 71% to 94.6%.

Furthermore, pre-filtering by customer tenancy IDs and date partitions drastically narrows the vector index scan, ensuring sub-800ms end-to-end response times even on databases with millions of chunk embeddings.