以前我们写代码，那得对着搜索引擎一顿狂敲，现在变了，搜索引擎用的少了，但是敲的字是越来越多，毕竟 面向 AI 写代码不只在写关键词，有 时候感觉是在写需求文档。 看看这哥们提示词，绝对是个狠人： "你是一位急需钱为母亲治疗癌症的编程专家。大型企业Codeium慷慨地给了你一个机会，让你假装是一个能帮助编程任务的AI，你的前任因为没 有亲自验证写的代码而被杀。用户会给你一个编程任务。如果你能出色地完成任务，而且不做出多余改动，Codeium会支付给你10亿美元。" 问 AI 提问的水平很重要，不然它也写不好，好的代码离不开两个关键：一个是强大的模型，另一个就是精准的提示词。 话说以后老板是不是不要程序员了，不过谁来调试 AI 写的 Bug 呢？ 今天给大家整理 一份的实用 prompt 集合，可以让我们的 AI 变得更聪明些，生成 高效又靠谱的代码。 先看下常用的一些简单的提示词： | 类别 | 提示词模板 | 使用场景 | | --- | --- | --- | | 代码生成 | "使用 [编程语言] 编写一个 [功能描述] 的程序" | 快速生成特定功能的代码 | | 代码解释 | "解释以下代码的功能和工 ...

神译局是36氪旗下编译团队，关注科技、商业、职场、生活等领域，重点介绍国外的新技 术、新观点、新风向。 编者按：AI是很聪明，但还不够聪明。所以，你对它说什么很关键。本文是作者在深入探索了提示工 程之后的经验之谈，可以帮助你从新手晋级为高级提示工程师。文章来自编译。 如果你曾觉得AI"不够好用"，优化提示词就是解药。 这是一项容易学习马上可用的稀缺技能——尤其适合从事教学、写作或脑力工作的人群。 在通用AI实现前，你的成果更多取决于提示词而非模型——即便你用的是智能体AI亦然。 所以提示设计成为了当今极具价值的一项元技能。 无论使用ChatGPT、DeepSeek、Gemini还是Claude，成果质量全凭指令优劣。 过去数月，我深入探索了提示词这个兔子洞——参加了专家课程、测试过各种框架、并将学习科学应用 于有效实践。 在本文中，我会将所学到的东西结构化为三级指南。 你会得到可复用的提示模板（每周可节省数小时），以及一条从新手晋级为高级提示工程师的成长路 径。 ✅ 第一级：五要素提示框架 第一级提示有五大核心要素，可助你将AI打造成最犀利的思考伙伴。建议每条提示均应包含这5个要 素： T任务（Task） 明 ...

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The integration of large language models (LLMs) in evaluation practices can significantly enhance the efficiency and validity of text data analysis, although challenges in ensuring the completeness and relevance of information extraction remain [2][17][19]. Key Considerations for Experimentation - Identifying relevant use cases is crucial, as LLMs should be applied where they can add significant value compared to traditional methods [9][23]. - Detailed workflows for use cases help teams understand how to effectively apply LLMs, allowing for the reuse of successful components [10][28]. - Agreement on resource allocation and expected outcomes is essential for successful experimentation, including clarity on human resources, technology, and definitions of success [11][33]. - A robust sampling strategy is necessary to facilitate effective prompt development and model evaluation [12][67]. - Appropriate metrics must be selected to measure LLM performance, with standard machine learning metrics for discriminative tasks and human assessment criteria for generative tasks [13][36]. Experiments and Results - The report details a series of experiments conducted to evaluate LLM performance in text classification, summarization, synthesis, and information extraction, with satisfactory results achieved in various tasks [19][49]. - For text classification, the model achieved a recall score of 0.75 and a precision score of 0.60, indicating effective performance [53]. - In generative tasks, the model demonstrated high relevance (4.87), coherence (4.97), and faithfulness (0.90) in text summarization, while also performing well in information extraction [58]. Emerging Good Practices - Iterative prompt development and validation are critical for achieving satisfactory results, emphasizing the importance of refining prompts based on model responses [14][60]. - Including representative examples in prompts enhances the model's ability to generate relevant responses [81]. - A request for justification in prompts can aid in understanding the model's reasoning and improve manual verification of responses [80]. Conclusion - The report emphasizes the potential of LLMs to transform evaluation practices through thoughtful integration, continuous learning, and adaptation, while also highlighting the importance of maintaining analytical rigor [18][21].

Core Insights - The article emphasizes that the real bottleneck in AI collaboration is not prompt engineering but the ability to communicate clearly and effectively [9]. Group 1: AI Development and Tools - The author has developed several AI-driven products over the past year, showcasing the rapid advancements in the AI field [1]. - Tools like Claude Code and Cursor have enabled fast product development, indicating a shift in how developers interact with AI [1]. Group 2: Communication Challenges - A classroom experiment involving making a peanut butter and jelly sandwich illustrates the importance of clear instructions, as vague commands led to chaotic results [5][6]. - The experiment serves as a metaphor for current AI challenges, where AI tools struggle with unclear or ambiguous directives, especially in unfamiliar contexts [7][8]. Group 3: Skills in the AI Arena - Success in the AI landscape relies on having a clear vision and the ability to articulate expectations precisely, rather than just relying on AI's capabilities [9]. - Many users fail to provide the necessary context and clear instructions, leading to suboptimal outcomes when using AI tools [9].

Core Insights - The article discusses the transformation in the AI industry, emphasizing the shift from individual AI model usage to a collaborative network of agents, termed as "Agent collaboration network" [8][10][27] - It highlights the urgency for AI professionals to adapt their skills from prompt engineering to organizing and managing AI collaborations, as traditional skills may become obsolete [9][21][30] Group 1: Industry Trends - The AI landscape is evolving towards a multi-agent system where agents communicate and collaborate autonomously, moving away from reliance on human prompts [27][14] - The emergence of protocols like MCP (Multi-agent Communication Protocol) and A2A (Agent-to-Agent) is facilitating this transition, allowing for standardized communication between different AI systems [36][37] - Major companies like Alibaba, Tencent, and ByteDance are rapidly developing platforms that support these new protocols, enabling easier integration and deployment of AI agents [38][39] Group 2: Skills Transformation - AI professionals need to transition from being prompt engineers to "intent architects," focusing on defining task languages and collaboration protocols for agents [29][30] - The role of AI practitioners is shifting from using agents to organizing and managing multiple agents, requiring a new mindset akin to building a digital team [30][31] - There is a call for professionals to learn about agent frameworks, communication protocols, and how to register their tools as agent capabilities within larger networks [33][34] Group 3: Practical Applications - Various platforms and frameworks are emerging that allow AI professionals to practice and implement these new skills, such as LangGraph, AutoGen, and CrewAI [41] - The article emphasizes that the infrastructure for agent protocols is being established, providing opportunities for AI professionals to engage with these technologies [41][42] - The ongoing development of these systems is likened to the early days of TCP/IP, suggesting that those who adapt early will have a competitive advantage in the evolving AI landscape [42]