RAG

Retrieval-Augmented Generation (RAG) is an innovative approach that enhances the capabilities of Large Language Models (LLMs) by integrating external information retrieval into the generation process. This technique allows models to access up-to-date and specialized knowledge without the need for extensive retraining. By combining the strengths of LLMs with a retrieval mechanism, RAG improves the accuracy and reliability of generated responses, making it particularly useful for applications requiring precise information. Additionally, RAG facilitates easier fact-checking by providing sources for the information used, thus addressing common challenges like hallucinations in AI-generated content.

Wild Wild RAG… (Part 1)

Towards AI

Let’s begin by understanding what exactly an RAG Application is, a term that has garnered significant attention in recent months. RAG (Retrieval-Augmented Generation) is an AI framework that enhances...

Explaining RAG in Layman’s Terms

The Pythoneers

Have you ever wondered how AI applications, including large language models (LLMs), can provide up-to-date information or even recall knowledge that wasn’t part of their original training? This is whe...

Around the R.A.G. in 80 Questions — Part I

Towards AI

R etrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of applied generative AI. A novel concept introduced by Lewis et. al., in their seminal paper Retrieval-Aug...

RAG Using Langchain

Python in Plain English

RAG, or Retrieval-augmented generation, is a method that boosts the precision and dependability of generative AI models by incorporating factual information retrieved from external sources. How does ...

Improving RAG Performance Using Rerankers

Towards Data Science

Introduction RAG is one of the first tools an engineer will try out when building an LLM application. It’s easy enough to understand and simple to use. The primary motive when using vector search is t...

A Simple Framework for RAG Enhanced Visual Question Answering

Towards Data Science

Empowering Phi-3.5-vision with Wikipedia knowledge for augmented Visual Question Answering. Photo by Christian Lue on Unsplash Introduction Retrieval Augmented Generation (RAG) is a powerful techniqu...

A Complete Guide to RAG

Towards AI

If you haven’t heard about RAG from your refrigerator yet, you surely will very soon, so popular this technique has become. Surprisingly, there is a lack of complete guides that consider all the nuanc...

Understanding RAG: Retrieval-Augmented Generation

Level Up Coding

Today, RAG stands as one of the most significant business applications of LLMs, making it increasingly vital to grasp its processes… Continue reading on Level Up Coding

Graph RAG — A conceptual introduction

Towards Data Science

Graph RAG — A Conceptual Introduction Graph RAG answers the big questions where text embeddings won’t help you. Retrieval Augmented Generation (RAG) has dominated the discussion around making Gen AI ...

Showcasing Different Approaches for Implementing Multilingual RAG

Towards AI

Image Generated by Microsoft Copilot Retrieval-Augmented Generation (RAG) Large language models inherently possess a significant body of factual relational knowledge [1]. However, these models still e...

Why Your RAG is Not Reliable in a Production Environment

Towards Data Science

You should not be fooled by the simplicity of this diagram. In fact, RAG hides a certain complexity and involves the following components behind the scenes: Loaders to parse external data in different...

Comprehensive Guide to Retrieval-Augmented Generation (RAG)

Towards AI

In the world of artificial intelligence, new techniques are constantly emerging to make machines smarter and more efficient. One such technique is Retrieval-Augmented Generation (RAG). But what exactl...