Building Production RAG Systems: Beyond the Tutorial

Most RAG tutorials stop at "embed your documents and query them." In production, that approach falls apart within weeks. After deploying RAG pipelines for enterprise clients across FinTech and E-commerce, here is what actually matters.

The Chunking Problem

The single biggest factor in RAG quality is not your embedding model — it is your chunking strategy. Naive fixed-size chunking destroys context. Instead, use semantic chunking that respects document structure: headers, paragraphs, code blocks, and tables should remain intact.

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=64,
    separators=["\n## ", "\n### ", "\n\n", "\n", " "]
)

Hybrid Search Outperforms Pure Vector

Pure vector similarity retrieval misses exact keyword matches. A hybrid approach combining dense vector search with BM25 sparse retrieval consistently outperforms either method alone. Pinecone and Weaviate both support hybrid natively.

Reranking Is Non-Negotiable

After initial retrieval, a cross-encoder reranker like Cohere Rerank or a fine-tuned model dramatically improves precision. The retrieval step casts a wide net; reranking ensures only the most relevant chunks reach the LLM context window.

Evaluation and Monitoring

Without automated evaluation, you are flying blind. Track retrieval precision, answer faithfulness, and hallucination rate. Tools like Ragas and Phoenix make this straightforward. Set up alerting on quality regressions before your users notice.

Key Takeaways

Production RAG is an engineering discipline, not a weekend project. Invest in chunking, hybrid search, reranking, and continuous evaluation. The results speak for themselves: our enterprise deployments consistently achieve 90%+ answer accuracy with sub-2-second latency.