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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-prompt-engineering-techniques/
课程评论：没有评论
**课程名称：AI - 提示工程技术** **课程概述：** 本课程是您掌握如何有效设计提示以最大化OpenAI GPT模型潜力的终极指南。无论您是需要解决复杂问题、构建AI驱动的应用，还是优化工作流程，本课程都将为您提供实用的技术和真实世界的案例，帮助您提升技能。 您将深入学习从零样本学习到先进的思维链（Chain-of-Thought, CoT）推理的各种提示工程技巧。课程将涵盖少样本学习、上下文学习和多步推理，并使用Python和LangChain等尖端工具进行实践。通过动手项目和最佳实践，您将能自信地将这些技术应用于实际场景。 **主要学习内容：** 1. **提示工程入门**： * 什么是提示工程及其重要性。 * 设计有效提示的原则。 * OpenAI GPT模型及其能力介绍。 2. **零样本与少样本学习**： * 零样本和少样本学习技术概述。 * 使用Python和真实案例进行实践。 * 少样本学习中的示例选择最佳实践。 3. **上下文学习**： * 上下文学习的理解及其应用。 * 通过示例设计提示以改进模型响应。 * 上下文学习的真实应用场景。 4. **思维链（CoT）提示**： * 通过CoT推理分解复杂问题。 * 比较CoT与标准提示的性能。 * 用于多步问题解决的高级CoT技术。 5. **Python与LangChain集成**： * LangChain库在提示工程工作流程中的应用。 * 使用LangChain和OpenAI模型构建交互式应用。 * 在Python中自动化和扩展基于提示的任务。 6. **评估与优化**： * 测试和优化提示的准确性和相关性。 * 性能评估：跨用例对比结果。 * 针对特定行业或挑战优化提示的技巧。 7. **动手项目**： * 为现实世界问题设计AI工作流程（内容创作、编码助手、客户支持等）。 * 使用LangChain和Python构建并部署AI驱动的应用。 **课程目标：** 成为提示工程领域的专家，掌握构建智能、有影响力的AI应用的实用技能，学习行业领先技术，并加入AI创新者社区。
Are you ready to revolutionize the way you interact with AI? This course, Prompt Engineering Using Python, is your ultimate guide to mastering the art and science of crafting effective prompts that maximize the potential of OpenAI's GPT models. Whether you're solving complex problems, building AI-powered applications, or enhancing workflows, this course is packed with actionable techniques and real-world examples to take your skills to the next level!From zero-shot learning to advanced chain-of-thought (CoT) reasoning, this course dives deep into the nuances of prompt engineering. You'll explore few-shot learning, in-context learning, and multi-step reasoning, using cutting-edge tools like Python and the LangChain library. With hands-on projects and best practices, you'll gain the confidence to apply these techniques to real-world scenarios.You will learn the following and more in this PRACTICAL COURSE1. Introduction to Prompt EngineeringWhat is prompt engineering, and why does it matter?The principles of crafting effective prompts.Introduction to OpenAI's GPT models and their capabilities.2. Zero-Shot and Few-Shot LearningOverview of zero-shot and few-shot learning techniques.Practical implementation in Python using real-world examples.Best practices for example selection in few-shot learning.3. In-Context LearningUnderstanding in-context learning and its applications.Designing prompts with contextual examples to improve model responses.Real-world scenarios for in-context learning.4. Chain of Thought (CoT) PromptingBreaking down complex problems with CoT reasoning.Comparing CoT performance against standard prompts.Advanced CoT techniques for multi-step problem-solving.5. Python and LangChain IntegrationIntroduction to the LangChain library for prompt engineering workflows.Building interactive applications with LangChain and OpenAI models.Automating and scaling prompt-based tasks in Python.6. Evaluation and OptimizationHow to test and refine prompts for accuracy and relevance.Performance evaluation: Comparing results across use cases.Tips for optimizing prompts for specific industries or challenges.7. Hands-On ProjectsDesign AI workflows for real-world problems (content creation, coding assistants, customer support, etc.).Build and deploy an AI-powered application using LangChain and Python.Are you ready to become a master in prompt engineering? This is more than just a course-it's your gateway to building intelligent, impactful AI applications. Gain practical skills, learn industry-leading techniques, and join a growing community of AI innovators. Don't wait-enroll now and start shaping the future with AI! Click Join Now to begin your journey to AI Prompt Engineering mastery!
