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Retrieval Augmented Generation

Building a Robust RAG App with Guardrails using Portkey and MongoDB

Building a Robust RAG App with Guardrails using Portkey and MongoDB

Introduction Retrieval-Augmented Generation (RAG) has become a go-to technique for enhancing LLMs with external data. In this tutorial, we'll build a production-ready RAG application in just 10 minutes using Portkey, MongoDB Atlas, Patronus AI, and LlamaIndex. But first, what is RAG? Retrieval-Augmented Generation (RAG) is a technique that enhances LLMs

Understanding RAG: A Deeper Dive into the Fusion of Retrieval and Generation

Understanding RAG: A Deeper Dive into the Fusion of Retrieval and Generation

Retrieval-Augmented Generation (RAG) models represent a fascinating marriage of two distinct but complementary components: retrieval systems and generative models. By seamlessly integrating the retrieval of relevant information with the generation of contextually appropriate responses, RAG models achieve a level of sophistication that sets them apart in the realm of artificial