Architecting a Production-Grade RAG System with LangChain and Redis Vector Search
This article walks through the complete architecture of a scalable Retrieval-Augmented Generation (RAG) system built with LangChain and Redis Vector Search. It breaks down each layer — from document ingestion and vector indexing to multi-tenant orchestration and LLM prompt optimization — with a strong focus on low-latency, production-grade design. Whether you’re building an AI assistant, enterprise chatbot or domain-specific retrieval layer, this guide offers real-world patterns, trade-offs and engineering tactics to get it right. If you’re planning to build something similar, get in touch — we help teams architect and scale AI-native systems that perform under pressure.
