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

Studying Retrieval Augmented Era

The Structure, Operational Layers, and Finest Practices for Efficient RAG Implementation

It might not come as a shock that retrieval augmented era (RAG) is among the many most utilized strategies on this planet of generative AI and enormous language model-powered functions. The truth is, in line with a Databricks report, greater than 60% of LLM-powered functions use RAG in some kind. Due to this fact, within the world LLM market, which is at present valued at around $6 Billion and growing at almost 40% YoY, RAG undoubtedly turns into a kind of essential strategies to grasp.

Constructing a PoC RAG pipeline shouldn’t be too difficult at the moment. There are available examples of code leveraging frameworks like LangChain or LlamaIndex and no-code/low-code platforms like RAGArch, HelloRAG, and so on.

A production-grade RAG system, however, consists of a number of specialised layers…