How to Build a Production-Ready RAG Pipeline Using n8n, Qdrant, and Gemini

A detailed technical guide outlines how to move a retrieval-augmented generation (RAG) system from a working demo to a reliable production pipeline. The walkthrough recommends splitting RAG into two separate workflows — one for document ingestion and one for query handling — to prevent slow processing tasks from blocking user-facing requests. It emphasizes storing rich metadata in Qdrant, including access control labels and version timestamps, to enable permission-based and freshness-aware retrieval. The guide also covers making ingestion idempotent and resumable so that partial failures during large document batches do not corrupt or duplicate data. Additional production measures discussed include rate-limit-safe embedding, Gemini prompt grounding with citations, retry logic, and evaluation frameworks to catch issues before end users encounter them.