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9 RAG Architectures Every AI Developer Must Know: A Complete Guide with Examples
Architectures beyond Naive Rag to build reliable production AI Systems
Your chatbot confidently told a customer that your return policy is 90 days. It’s 30.It later describes features your product does not even have.
That is the gap between a great demo and a real production system. Language models sound sure even when they are wrong, and in production that gets expensive fast.
This is why serious AI teams use RAG. Not because it is trendy, but because it keeps models grounded in real information.
What most people miss is that there is no single RAG. There are multiple architectures, each solving a different problem. Pick the wrong one, and you waste months.
This guide breaks down the RAG architectures that actually work in production.
Let’s start by Learning about Rag.
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