RAG 检索增强生成实战课：提升 ChatGPT 与大语言模型智能水平的核心技术与开发框架

本课程系统讲解如何通过 RAG（检索增强生成）机制提升 ChatGPT 与 LLM 的上下文理解与知识调用能力。你将学习 RAG 架构原理、向量数据库与嵌入技术、文档切分策略、信息检索流程，并实战使用 LangChain 与 Flowise 搭建完整 RAG 系统。课程还涵盖开源模型部署、数据隐私保护与性能评估方法，适合希望打造高效智能问答系统的开发者与 AI 应用实践者。

本教程为高质量英文原版视频课程，英文基础较弱的同学可通过翻译插件辅助学习。

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教程名称：RAG: Raising the Potential of ChatGPT LLMs to the next level

下载连结：https://www.nidown.com/chatgpt-423115.html

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英文原版介绍

Published 7/2024
Created by Data Bootcamp
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 79 Lectures ( 4h 31m ) | Size: 1.75 GB

Learn how to implement RAGs to enrich the knowledge of ChatGPT and LLMs, increasing their effectiveness and capabilities

What you’ll learn:

Introduction to Generative AI and Large Language Models
Techniques for Improving LLMs
Fundamentals of Retrieval Augmented Generation (RAG)
Applications of RAGs
Tools for the development of a RAG
Custom GPTs
Langchain
Components of the RAG
Flowise the perfect framework for the development of RAGs
Indexing Pipeline and RAG Pipeline
Document Fragmentation
Embeddings and Vector Databases
Information search and retrieval
Open-source LLMs for RAGS: the best ally for data protection and privacy
RAG performance evaluation

Requirements:

not needed