Building a RAG System That Actually Works in Production

Why Most RAG Fails

Most RAG demos work on five documents and break at five hundred. The failure modes are predictable: bad chunking, naive retrieval, no evaluation, and zero guardrails. A production RAG system is an engineering problem, not a prompt problem.

Chunking Matters Most

How you split your documents determines everything. Fixed-size chunks ignore document structure. Sentence-aware chunking preserves context. Hierarchical chunking — small chunks for retrieval, parent chunks for generation — gives the model context without bloating the prompt. Spend time here before touching your embedding model.

Retrieval Is Not Just Similarity

Vector similarity is necessary but not sufficient. Add hybrid search (keyword + vector), metadata filtering, and re-ranking. A cross-encoder re-ranker on top of your retriever can lift answer accuracy by 15-30%. It is the single highest-leverage improvement you can make.

Evaluation and Guardrails

If you are not measuring retrieval precision, answer faithfulness, and citation accuracy, you are flying blind. Build a golden test set of questions and expected answers. Run it on every change. Add guardrails: source attribution, refusal when confidence is low, and output validation. Production RAG without evaluation is a liability.