RAG pipelines look simple in tutorials. In production, they're anything but.
After deploying 40+ RAG systems for enterprise clients, we've distilled the architecture into five layers that matter:
Chunking strategy — Semantic chunking with overlap outperforms fixed-size splitting by 23% on retrieval accuracy. Use sentence-transformers for boundary detection.
Embedding selection — Don't default to OpenAI embeddings. For domain-specific content, fine-tuned embeddings improve recall by 15-30%. Test with your actual data.
Hybrid search — Combine dense vector search with BM25 keyword search. Pure vector search misses exact matches for product codes, legal citations, and technical terms.
Reranking — A cross-encoder reranker on top-20 results improves answer quality more than any prompt engineering trick. Cohere Rerank and BGE-reranker are our go-tos.
Evaluation framework — Build a golden dataset of 100+ question-answer pairs from your domain. Measure faithfulness, relevance, and context precision on every deployment.
