RAG

Retrieval-Augmented Generation (RAG) is an innovative approach that enhances the capabilities of AI, particularly large language models (LLMs). By integrating external knowledge sources, RAG allows these models to access real-time information and specific data that may not have been included during their initial training. This hybrid method improves the accuracy and relevance of AI-generated responses, addressing common issues such as outdated knowledge and the tendency to fabricate information. RAG is increasingly recognized as a vital technique in the field of generative AI, making it essential for applications that require up-to-date and contextually relevant answers.

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...

Diving into Retrieval-Augmented Generation (RAG): Complete Guide

Towards AI

What is RAG? Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by combining them with external knowledge retrieval systems. Instead of relying solely on th...

RAG: Explained Simply

Towards AI

Every LLM you have ever talked to learned everything it knows by reading an enormous amount of text before you ever typed a single word. Books, research papers, websites, code, and Wikipedia. Billions...

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 ...

Building RAG Systems: A Complete Guide

Towards AI

Imagine you ask ChatGPT about your company’s internal refund policy. It either makes something up or tells you it doesn’t know. That’s not a model problem, that’s a data problem. The model was never t...

Production RAG: From Anti-Patterns to Platform Engineering

Towards AI

RAG is a distributed system . It becomes clear when moving beyond demos into production. It consists of independent services such as ingestion, retrieval, inference, orchestration, and observability. ...

RAG (Retrieval-Augmented Generation)

Level Up Coding

The AI Brain With a Library Card Continue reading on Level Up Coding

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...