Building Production RAG Pipelines with Vector Embeddings & Hybrid Search

Retrieval-Augmented Generation (RAG) grounds generative AI models with proprietary enterprise documentation to eliminate hallucinations.

1. Anatomy of a Production RAG System

Raw documents are split into semantic chunks, vectorized via high-dimensional embedding models, and stored in indexed vector databases.

2. Optimal Chunking & Overlap Strategies

Recursive character text splitting with 15% sliding window overlap preserves context across sentence boundaries.

3. Hybrid Search: Vector + BM25 Full-Text

Combining dense vector similarity with sparse BM25 keyword matching and cross-encoder re-ranking maximizes document recall accuracy.