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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-retrieval-augmented-generation/
课程评论：没有评论
课程名称：高级检索增强生成（Advanced Retrieval Augmented Generation） 课程概述： 本课程深入探讨了检索增强生成（RAG）的前沿技术，旨在帮助您掌握生成式人工智能（Generative AI）和大型语言模型（LLM）的高级应用。课程内容经过精心设计，旨在增强您的LLM实现，解决诸如速率限制和冗余数据等常见挑战，确保系统的稳健性、效率和可扩展性。 您将学习到： - 实施结构化输出以增强LLM调用的稳健性。 - 掌握异步Python，提高LLM调用的速度和性价比。 - 生成合成数据，为RAG系统建立强有力的基准，即使没有活跃用户。 - 过滤冗余生成的数据，提高系统效率。 - 利用缓存、追踪和重试机制克服OpenAI的速率限制。 - 结合缓存、追踪和重试技术实现最佳性能。 - 安全保护API密钥，并使用最佳实践简化开发过程。 - 应用高级代理模式构建灵活和适应性强的AI系统。 课程内容： - RAG概述与结构化输出：为RAG概念打下坚实基础，了解结构化输出在代理模式中的重要性。 - 设置与配置：逐步指导您如何使用Docker、Python及相关工具配置开发环境。 - 异步执行与缓存：学习如何并发执行多个LLM调用并实施缓存策略以节省时间和资源。 - 合成数据生成：创建高质量的合成数据集以模拟现实场景，优化RAG系统。 - 高级故障排除：掌握异步代码的调试技巧，处理OpenAI速率限制等复杂挑战。 课程要求： - 现代笔记本电脑，已安装Python，或可访问Google Drive。 - 软件工程师经验（优先考虑2年以上）。 - 中级Python编程技能或快速学习能力。 - 基本的数据科学知识（精准度、召回率、pandas）。 - 访问ChatGPT专业版或同等LLM工具。 适合人群： - 有基础RAG实现经验的软件工程师，希望提升技能。 - 旨在优化基于LLM系统的数据科学家和AI专业人士。 - 有兴趣掌握最新RAG技术以构建稳健、可扩展AI解决方案的开发人员。 立即加入本课程，运用最新的高级RAG技术转变您的AI系统！
Master Advanced Retrieval Augmented Generation (RAG) with Generative AI & LLMUnlock the Power of Advanced RAG Techniques for Robust, Efficient, and Scalable AI SystemsCourse Overview:Dive deep into the cutting-edge world of Retrieval Augmented Generation (RAG) with this comprehensive course, meticulously designed to equip you with the skills to enhance your Large Language Model (LLM) implementations. Whether you're looking to optimize your LLM calls, generate synthetic datasets, or overcome common challenges like rate limits and redundant data, this course has you covered.What You'll Learn:Implement structured outputs to enhance the robustness of your LLM calls.Master asynchronous Python to make your LLM calls faster and more cost-effective.Generate synthetic data to establish a strong baseline for your RAG system, even without active users.Filter out redundant generated data to improve system efficiency.Overcome OpenAI rate limits by leveraging caching, tracing, and retry mechanisms.Combine caching, tracing, and retrying techniques for optimal performance.Secure your API keys and streamline your development process using best practices.Apply advanced agentic patterns to build resilient and adaptive AI systems.Course Content:Introduction to RAG and Structured Outputs: Gain a solid foundation in RAG concepts and learn the importance of structured outputs for agentic patterns.Setup and Configuration: Step-by-step guidance on setting up your development environment with Docker, Python, and essential tools.Asynchronous Execution & Caching: Learn to execute multiple LLM calls concurrently and implement caching strategies to save time and resources.Synthetic Data Generation: Create high-quality synthetic datasets to simulate real-world scenarios and refine your RAG system.Advanced Troubleshooting: Master debugging techniques for async code and handle complex challenges like OpenAI rate limits.Requirements:A modern laptop with Python installed or access to Google Drive.Experience as a software engineer (2+ years preferred).Intermediate Python programming skills or ability to learn quickly.Basic understanding of data science (precision, recall, pandas).Access to a pro version of ChatGPT or equivalent LLM tools.Who Should Enroll:Software engineers with experience in basic RAG implementations who want to advance their skills.Data scientists and AI professionals looking to optimize their LLM-based systems.Developers interested in mastering the latest RAG techniques for robust, scalable AI solutions.Join this course today and transform your AI systems with the latest Advanced RAG techniques!
