RAG: Raising the Potential of ChatGPT LLMs to the next level

本课程《掌握 RAG：用检索增强生成技术提升 ChatGPT 与大语言模型能力》由 Data Bootcamp 团队推出，专为希望深入理解并实操 RAG（Retrieval Augmented Generation）系统的 AI 从业者与技术爱好者设计。你将学习如何通过引入实时外部知识，大幅增强 ChatGPT 与其他 LLM 的准确性、上下文理解能力与业务实用性。

课程涵盖生成式 AI 与大语言模型基础、RAG 架构与关键组件（如嵌入、向量数据库、文档切片、索引流程等），并结合 Flowise、LangChain、LlamaIndex 等热门工具，从零搭建完整 RAG 系统。还包括开源模型在数据隐私保护中的优势与 RAG 性能评估方法。课程支持无代码学习，无需编程经验，适合希望构建更强大、更可靠 AI 应用的初学者与专业人士。立即加入，全面提升你的语言模型实战能力！

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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