RAGOps Guide: Building and Scaling Retrieval Augmented Generation Systems | by Abhinav Kimothi | Nov, 2024

Learning Retrieval Augmented Generation

The Architecture, Operational Layers, and Best Practices for Effective RAG Implementation

It may not come as a surprise that retrieval augmented generation (RAG) is among the most applied techniques in the world of generative AI and large language model-powered applications. In fact, according to a Databricks report, more than 60% of LLM-powered applications use RAG in some form. Therefore, in the global LLM market, which is currently valued at around $6 Billion and growing at almost 40% YoY, RAG undoubtedly becomes one of those crucial techniques to master.

Building a PoC RAG pipeline is not too challenging today. There are readily available examples of code leveraging frameworks like LangChain or LlamaIndex and no-code/low-code platforms like RAGArch, HelloRAG, etc.

A production-grade RAG system, on the other hand, is composed of several specialised layers…