How to Build a Full-Stack RAG Pipeline Using React, Node.js, and MongoDB

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A technical walkthrough published on DEV Community explains how to extend a standard React-Express-MongoDB stack with a Retrieval-Augmented Generation (RAG) pipeline for AI-powered applications. The architecture works by splitting uploaded documents into smaller chunks, converting each chunk into vector embeddings, and storing them in MongoDB Atlas for semantic search. When a user submits a question, it is also converted into an embedding and compared against stored vectors to retrieve the most relevant document sections. Those sections are then assembled into a prompt and passed to a large language model, which generates a context-aware response returned to the React frontend. The article also highlights production considerations such as authentication, rate limiting, caching, and hybrid search to move beyond a basic demo.

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