Building Production-Ready RAG Systems That Actually Scale

Chunking is a product decision, not a default

The way you split documents determines what the model can retrieve. Chunks that are too large dilute relevance; chunks that are too small lose the context needed to answer well. We tune chunk size and overlap per content type — a legal contract behaves nothing like a support FAQ.

Wherever possible we chunk along semantic boundaries (headings, sections, list items) rather than fixed token windows, and we attach metadata like source, section, and timestamp so retrieval can be filtered and answers can be cited.

Embeddings and retrieval quality

Embedding choice matters more than most teams expect. We benchmark a few models against a representative query set before committing, and we frequently combine dense vector search with keyword (BM25) search in a hybrid retriever to catch exact-match terms that embeddings miss.

Re-ranking the top candidates with a cross-encoder is one of the highest-leverage improvements you can make — it consistently lifts answer quality with minimal added latency.

Evaluation before you ship

You cannot improve what you cannot measure. We build an evaluation set of real questions with known-good answers and track retrieval precision, answer faithfulness, and hallucination rate on every change. Automated LLM-as-judge scoring catches regressions before they reach production.

Guardrails that keep answers grounded

A production RAG system should refuse gracefully when it doesn't have the context, cite its sources, and never invent facts. We enforce grounding by instructing the model to answer only from retrieved context, surfacing citations, and adding a fallback path for low-confidence queries.