rag-in-action and RAG-To-Know
About rag-in-action
huangjia2019/rag-in-action
End-to-end RAG system design, evaluation, and optimization. 极客时间RAG训练营，RAG 10大组件全面拆解，4个实操项目吃透 RAG 全流程。RAG的落地，往往是面向业务做RAG，而不是反过来面向RAG做业务。这就是为什么我们需要针对不同场景、不同问题做针对性的调整、优化和定制化。魔鬼全在细节中，我们深入进去探究。
This project helps AI developers build sophisticated Retrieval-Augmented Generation (RAG) systems. It guides you through integrating your documents into large language models to generate accurate, context-aware responses. You provide raw data like PDFs and get a fully functional RAG application tailored for specific business needs.
About RAG-To-Know
CornelliusYW/RAG-To-Know
The repository explores various RAG techniques, including implementation guides, use cases, and best practices. Each article is designed to help researchers, developers, and enthusiasts understand and implement RAG systems efficiently.
This repository provides comprehensive guides and code examples to help you understand and implement Retrieval Augmented Generation (RAG) systems. It helps you take raw information and a user query to generate accurate, contextually relevant answers using various techniques. This is ideal for AI researchers, machine learning engineers, and developers working with large language models.
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