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所在平台: Udemy |
课程主页: https://www.udemy.com/course/fundamentals-of-ragretrieval-augmented-generation/
课程评论：没有评论
课程名称：RAG基础（检索增强生成） 课程概述：释放生成式人工智能的力量，学习检索增强生成（RAG）！在当今快速发展的AI领域，传统的语言模型无论多么庞大，都会面临一个共同的局限：它们受到训练数据静态性质的限制。随着世界的变化和新知识的不断产生，仅依靠预训练模型可能会导致过时或不完整的答案。检索增强生成（RAG）正是为了解决这个问题。 本课程旨在帮助您理解并应用这种前沿架构，该架构将信息检索的动态优势与大型语言模型（LLMs）的生成能力相结合。无论您是在构建AI代理、聊天机器人、智能助手还是增强搜索的应用程序，RAG都将成为您解决方案的基石。 学习内容包括： - 为什么传统的LLMs在动态实时或特定领域的信息方面会显得不足，以及RAG如何填补这一空缺 - RAG的核心组件：检索（从外部知识库中搜索）和生成（使用LLMs产生丰富的响应） - 如何使用流行工具和框架从零开始设计、构建和部署RAG系统 - 通过实践项目巩固学习 实践案例： 我们将指导您完成两个实际的RAG实现，这些实现可应用并扩展到您的项目中： 1. LiveStockIQ：一个与实时金融API集成的股市助手，提供当前股市数据、公司信息和市场趋势。您将看到检索如何连接到API，以及LLMs如何生成洞察。 2. SmartRecruit：一个为人力资源团队提供智能简历分析和职位描述匹配的AI招聘助手，使用上下文文档检索和总结技术。 适合人群： - AI/ML工程师和数据科学家，想提升生成AI技能 - 构建智能搜索与助手解决方案的开发者 - 探索生成AI实际应用的产品经理与创新者 - 对如何超越训练数据创建动态响应系统感兴趣的任何人 通过本课程的学习，您不仅会理解什么是RAG，还能够实施、定制并将其集成到自己的AI解决方案中。准备好提升您的生成AI项目水平吧，学习RAG基础！
Unlock the Power of Generative AI with Retrieval-Augmented Generation (RAG)!In today's rapidly evolving AI landscape, traditional language models-no matter how large-face a common limitation: they are bound by the static nature of their training data. As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.That's where Retrieval-Augmented Generation (RAG) comes in.This course, Fundamentals of RAG, is designed to help you understand and apply this cutting-edge architecture that combines the dynamic strengths of information retrieval with the generative power of large language models (LLMs). Whether you're building AI agents, chatbots, intelligent assistants, or search-enhanced applications, RAG will become a cornerstone of your solution.We'll start by demystifying RAG's architecture and real-world importance:What You'll Learn:Why traditional LLMs fall short when it comes to dynamic, real-time, or domain-specific information-and how RAG fills the gapThe core components of RAG: Retrieval (searching from external knowledge bases) and Generation (using LLMs to produce rich responses)How to design, build, and deploy RAG systems from scratch using popular tools and frameworksHands-on projects to help reinforce learning through practical applicationHands-On Use Cases:We'll guide you through two real-world RAG implementations that you can apply and extend in your own projects:LiveStockIQ - A stock market assistant that integrates with real-time financial APIs to provide current stock data, company info, and market trends. You'll see how retrieval connects to APIs and how LLMs generate insights on top of it.SmartRecruit - An AI-powered recruitment assistant for HR teams that intelligently analyzes resumes and matches them to job descriptions using contextual document retrieval and summarization.Who Is This Course For?This course is perfect for:AI/ML engineers and data scientists looking to level up their GenAI skillsDevelopers building intelligent search and assistant solutionsProduct managers and innovators exploring real-world applications of GenAIAnyone curious about how LLMs can go beyond training data to create dynamic, responsive systemsBy the end of this course, you won't just understand what RAG is-you'll be able to implement it, customize it, and integrate it into your own AI solutions.Get ready to take your Generative AI projects to the next level with the Fundamentals of RAG!
