Building Production-Ready RAG Applications: A Complete Guide

RAG is a retrieval problem first

Most “the model hallucinated” tickets are retrieval failures. If the right paragraph never enters the prompt, no amount of prompt poetry will save the answer.

Treat the corpus as a product: owners, freshness, access control, and a way to retire stale PDFs. A vector index of last year’s price list is worse than no AI at all.

Chunking that matches how people ask

Fixed 512-token windows are a starting point, not a strategy. Policies, product SKUs, and API docs have different shapes. Prefer heading-aware splits, keep tables intact, and store metadata (source URL, product, locale, last updated).

Overlap helps, but too much overlap pollutes ranking. Evaluate on real questions from sales and support — not synthetic trivia.

• Store source title, URL, and section heading with every chunk.
• Separate “how-to” content from “legal/policy” content in filters.
• Re-embed when a source changes; do not rely on a one-time ingest.

Retrieval, rerank, then generate

Hybrid search (keyword + vectors) still wins on SKUs, error codes, and proper nouns. A cross-encoder reranker on the top 20 hits is often the cheapest quality jump after a naive top-k.

Ground the model: cite the chunks, refuse when similarity is low, and never let the model invent a policy. Return “I don’t have this in the knowledge base” as a first-class outcome.

Evaluation before you ship

Build a golden set of 50–100 real questions with expected sources. Track answer faithfulness, citation accuracy, and latency. Offline evals catch regressions when you change chunk size or the embedding model.

In production, log unanswered questions. That queue is your next content sprint — and a better roadmap than adding another model.