retrieval-augmented-generation and RAG-Overview

RAG-Overview
36
Emerging
Maintenance 10/25
Adoption 7/25
Maturity 16/25
Community 20/25
Maintenance 2/25
Adoption 7/25
Maturity 15/25
Community 12/25
Stars: 33
Forks: 24
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 28
Forks: 4
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No Package No Dependents
Stale 6m No Package No Dependents

About retrieval-augmented-generation

VectorInstitute/retrieval-augmented-generation

Reference Implementations for the RAG bootcamp

This collection provides examples for building applications that can answer questions using up-to-date or private information, going beyond what a large language model was originally trained on. You input a question and relevant external data (like documents, web pages, or database records), and it outputs an accurate, specific answer. It's designed for developers, data scientists, and AI engineers looking to create smart assistants or search tools.

AI development natural language processing information retrieval question answering data integration

About RAG-Overview

ALucek/RAG-Overview

An intuitive approach towards understanding how Retrieval Augmented Generation (RAG) systems work, for the curious yet daunted reader

This resource helps anyone curious about how Retrieval Augmented Generation (RAG) systems function, especially if you've felt intimidated by the technical details. It explains how providing relevant, current, or specialized information alongside a question can dramatically improve the accuracy of large language model responses. The target audience is non-technical professionals who want to grasp the core concepts of RAG without diving into code.

AI-explainability LLM-understanding business-intelligence knowledge-management AI-strategy

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