21 Lessons, Get Started Building with Generative AI 🔗https://microsoft.github.io/generative-ai-for-beginners/ aiazuretransformersopenaigptlanguage-modelsemantic-searchdall-eprompt-engineeringllmsgenerative-ai
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Prompt Engineering: The Career of Future Prompt engineering davinci-003 on our own docs for automated support (Part I) Prompt Engineering Guide: How to Engineer the Perfect Prompts Prompt Engineering in GPT-3 Prompt Engineering Template Prompt Engineering Topic by GitHub Prompt Engineering: The Ultim...
该平台提供了丰富的AI模型开发工具和资源,能够帮助你更加高效地利用GitHub Copilot等AI编程助手,实现更加复杂、高级的编程任务。 六、总结 GitHub Copilot与Prompt Engineering的结合为开发者带来了前所未有的编码体验。通过掌握Prompt Engineering的最佳实践,你可以更加高效地利用Copilot的潜力,提高编码效率和准确性。同时,...
首先,让我们从不熟悉 GitHub Copilot 或 prompt engineering 的人的基础知识开始。 什么是 GitHub Copilot ? GitHub Copilot 是一个 AI 结对开发工程师被 GitHub 开发并且 GitHub Copilot 由 OpenAI Codex 提供支持,OpenAI CodeX 是 OpenAI 创建生成式预训练语言模型。它根据注释和代码的上下文提供上下文话的代码建...
之前的文章中我们结合prompt-engineering课程体验了下混元大模型,今天再介绍大家一些写好prompt的思路以及prompt资料。 1 prompt工程是什么 1.1 什么是Prompt? LLM大语言模型终究也只是一个工具,我们不可能每个人都去训一个大模型,但是我们可以思考如何利用好大模型,让他提升我们的工作效率。就像计算器工具一样,要你算...
Prompt Engine isavailableon GitHub for free. 5. PromptLayer PromptLayer is a comprehensive prompt engineering application for creating, testing, deploying and monitoring prompts. The tool includes a prompt registry, which lets users create, version and retrieve prompts; batch testing; advanced search ...
This module highlights the basic core principles of prompt engineering and explains the best practices to create prompts quickly and effectively in GitHub Copilot. Learn the strategies that transform comments into precise code, identify the steps within
This module highlights the basic core principles of prompt engineering and explains the best practices to create prompts quickly and effectively in GitHub Copilot. Learn the strategies that transform comments into precise code, identify the steps within
吴恩达《ChatGPT Prompt Engineering for Developers》课程中文版,主要内容为指导开发者如何构建 Prompt 和 利用 OpenAI API 构建新的 LLM 的应用,包括: Prompt 的原则; 文本总结(如总结用户评论); 文本推断…