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！
原版英文介绍
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
Description:
This course is designed specifically for professionals who want to unlock the full potential of language models such as ChatGPT through Retrieval Augmented Generation Systems (RAGS). We will delve into how RAGS transform these language models into high-performance, expert tools across multiple disciplines by providing them with direct, real-time access to relevant, up-to-date information.Importance of RAGS in Language ModelsRAGS are fundamental to the evolution of large language models (LLMs), such as ChatGPT. Through the integration of external knowledge in real time, these systems enable LLMs to not only access a vast amount of up-to-date information but also learn and adapt to new information on a continuous basis. This retrieval and learning capability significantly improves text generation, allowing models to respond with unprecedented accuracy and relevance. This knowledge enrichment is crucial for applications that demand high accuracy and contextualization, opening up new possibilities in fields such as healthcare, financial analysis, and more.Course ContentGenerative AI and RAG FundamentalsIntroduction to assisted content generation and language models.Classes on the fundamentals of generative AI, key terms, challenges and evolution of LLMs.Impact of generative AI in various sectors.In-depth study of Large Language ModelsIntroduction and development of LLMs, including base models and tuned models.Exploration of the current landscape of LLMs, their limitations and how to mitigate common pitfalls such as hallucinations.Access and Use of LLMsHands-on use of ChatGPT, including hands-on labs and access to the OpenAI API.LLM OptimizationAdvanced techniques for improving model performance, including RAG with Knowledge Graphs and custom model development.Applications and Use Cases of RAGsDiscussion of the benefits and limitations of RAGs, with examples of real implementations and their impact in different industries.RAG Development ToolsInstruction on the use of specific tools for RAG development, including No-Code platforms such as Flowise, LangChain and LlamaIndex.Technical and Advanced RAG ComponentsDetails on RAG architecture, indexing pipelines, document fragmentation and the use of embeddings and vector databases.Hands-on Labs and ProjectsSeries of hands-on labs and projects that guide participants through the development of a RAG from start to finish, using tools such as Flowise and LangChain.MethodologyThe course alternates between theoretical sessions that provide an in-depth understanding of RAGS and hands-on sessions that allow participants to experiment with the technology in controlled, real-world scenarios.This program is perfect for those who are ready to take the functionality of ChatGPT and other language models to never-before-seen levels of performance, making RAGS an indispensable tool in the field of artificial intelligence.RequirementsNo previous programming experience is required. The course will include the use of No-Code tools to facilitate the learning and implementation of RAGS.
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