Building Your First RAG System: A Complete Step-by-Step Guide

Author(s): MahendraMedapati

Originally published on Towards AI.

Stop talking theory and start building — Create a working RAG system that can answer questions about your own documents

Retrieval-Augmented Generation (RAG) has become one of the most practical applications of AI for working with your own documents and data. Instead of just talking about how RAG works, let’s build a complete system from scratch that you can actually use.

In this article, you will learn how to build a complete Retrieval-Augmented Generation (RAG) system from scratch that can ingest documents, create embeddings, store them in a vector database, and answer user queries—all while keeping the coding beginner-friendly. The tutorial outlines the essential steps involved, including document ingestion, text chunking, embedding generation, and query processing. It also covers how to set up the environment and suggests different versions of the system using either free or paid service models, alongside testing tips and common issues. Finally, readers are encouraged to enhance their systems with additional features based on their needs.

Read the full blog for free on Medium.

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Published via Towards AI