RAG (Retrieve, Answer, Generate) is a framework and model for open-domain question answering (QA) that combines information retrieval, document ranking, and natural language generation techniques to generate high-quality answers to user queries. It uses a retriever component to retrieve relevant documents, a reader component to extract answers from retrieved documents, and a generator component to generate natural language responses. Readers can explore RAG's architecture, training methodology, and applications in various QA tasks, such as factoid QA, passage retrieval, and document summarization, understanding its potential to improve QA systems' performance and usability.

Building Smarter Chatbots With Advanced Language Models

Indexing and Routing Strategies in Retrieval-Augmented Generation (RAG) Chatbots

Crafting QA Tool with Reading Abilities Using RAG and Text-to-Speech

Enhancing AI Coding Assistants with Context Using RAG and SEM-RAG

Let’s RAG some financial reports with LLMware and ChromaDB

How We Saved 10s of Thousands of Dollars Deploying Low Cost Open Source AI Technologies At Scale with Kubernetes

Local RAG From Scratch

Getting Started With OpenAI’s GPT Builder, and How It Uses RAG

Do Enormous LLM Context Windows Spell the End of RAG?

fzliu/radient: Radient turns many data types