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

本課程《掌握 RAG：用檢索增強生成技術提升 ChatGPT 與大語言模型能力》由 Data Bootcamp 團隊推出，專為希望深入理解並實操 RAG（Retrieval Augmented Generation）系統的 AI 從業者與技術愛好者設計。你將學習如何通過引入實時外部知識，大幅增強 ChatGPT 與其他 LLM 的準確性、上下文理解能力與業務實用性。

課程涵蓋生成式 AI 與大語言模型基礎、RAG 架構與關鍵組件（如嵌入、向量資料庫、文檔切片、索引流程等），並結合 Flowise、LangChain、LlamaIndex 等熱門工具，從零搭建完整 RAG 系統。還包括開源模型在數據隱私保護中的優勢與 RAG 性能評估方法。課程支持無代碼學習，無需編程經驗，適合希望構建更強大、更可靠 AI 應用的初學者與專業人士。立即加入，全面提升你的語言模型實戰能力！

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

下載連結：https://www.nidown.com/chatgpt-423115.html

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最便捷、最實惠的 ChatGPT Plus 升級服務來了！！！

點擊查看詳情：https://www.kkmac.com/go/chatgpt

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英文原版介紹

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