From Zero to RAG (Part 1) (turtosa.com)

In a significant development for the AI community, a new series titled "From Zero to RAG" has been launched, aimed at demystifying the construction of Retrieval-Augmented Generation (RAG) systems. This initial installment offers a step-by-step guide on setting up a RAG pipeline, simplifying the seemingly complex process of building AI-enhanced products that utilize both parsing and retrieval systems. Topics covered include setting up a vector database, embedding models, text chunking, and query generation, supported by practical code examples available in a dedicated repository. The significance of this series lies in its potential to democratize access to advanced AI methodologies like RAG, which many companies are integrating into their product offerings by 2025. By breaking down technical jargon and providing hands-on instructions for using vector databases, like Infinity, and embedding models, it empowers developers to create customized RAG systems without needing extensive expertise. The article highlights crucial aspects of the RAG framework, such as chunk normalization, vector embedding, and search strategies, making it an invaluable resource for those looking to harness the power of AI/ML technologies effectively.