Core Viewpoint - Context engineering is emerging as a crucial skill for AI engineers, shifting the focus from traditional prompt engineering to providing structured and dynamic context for large language models (LLMs) to perform tasks effectively [3][7][15]. Group 1: Definition and Importance of Context Engineering - Context engineering involves constructing dynamic systems that provide accurate information and tools in the right format, enabling LLMs to complete tasks effectively [9][10]. - The significance of context engineering lies in its ability to address common failures in AI systems, which often stem from inadequate context or incorrect information being provided to the model [12][15]. - Unlike prompt engineering, which focuses on crafting clever prompts, context engineering emphasizes the importance of delivering complete and structured context to enhance model performance [17][19]. Group 2: Components of Effective Context Engineering - Effective context engineering requires accurate information, as models cannot infer context without being explicitly provided with it [12][19]. - The format of the context is critical; how information is communicated to the LLM can significantly impact its responses [13][19]. - Tools must be appropriately utilized to access external information, and the returned data should be formatted in a way that is easily understandable by the LLM [20]. Group 3: Transition from Prompt Engineering to Context Engineering - The transition from prompt engineering to context engineering is driven by the increasing complexity of applications, highlighting the need for a more comprehensive approach to context provision [16][17]. - Context engineering can be viewed as a subset of prompt engineering, where the focus shifts from single input prompts to managing and formatting dynamic data sets [17][18].

Core Insights - The article discusses the launch of PromptPilot, an intelligent solution platform designed for large models, which aims to transform vague user ideas into precise AI instructions, ensuring high-quality output from models [1][2]. Group 1: Product Features - PromptPilot automates the entire lifecycle of prompt generation, debugging, optimization, and iteration, freeing users from tedious tasks [3]. - The platform acts as a "demand translator," helping users clarify their needs through interactive guidance [3]. - It simplifies the process of defining ideal answers by allowing users to select from diverse generated responses, facilitating quick understanding of user intent [3][4]. - PromptPilot incorporates a closed-loop optimization system that turns "Bad Cases" into data assets for continuous improvement [3][4]. Group 2: Advanced Capabilities - The platform simulates human-like reflection and error summarization, enabling automatic iterative optimization to find the "golden question" for stable results [4]. - It supports multi-turn dialogue optimization, allowing for real-time feedback and enhancement in complex conversational scenarios [5]. - PromptPilot can optimize prompts for multi-modal scenarios, breaking down tasks into multiple steps and searching for optimal solutions [5]. - It enhances function call scenarios by optimizing both the triggering instructions and the descriptions of tools needed during task execution [5]. Group 3: User Accessibility - Users can easily integrate PromptPilot through an SDK, enabling automatic monitoring of "Bad Cases" and initiating a new round of prompt optimization [6]. - The platform standardizes the prompt engineering process, making it accessible for businesses and developers to focus on innovation in AI applications [6][7].

Core Insights - Anthropic's new research on multi-agent systems highlights the advantages of using multiple AI agents for complex research tasks, emphasizing their ability to adapt and explore dynamically [2][3][6][7]. Multi-Agent System Advantages - Multi-agent systems excel in research tasks that require flexibility and the ability to adjust methods based on ongoing discoveries, as they can operate independently and explore various aspects of a problem simultaneously [7][8]. - Anthropic's internal evaluations show that their multi-agent system outperforms single-agent systems by 90.2% in breadth-first query tasks [8]. - The architecture allows for efficient token consumption, with multi-agent systems demonstrating a significant performance boost compared to single-agent models [9][10]. System Architecture - The multi-agent architecture follows a "coordinator-worker" model, where a lead agent coordinates tasks among several specialized sub-agents [14][18]. - The lead agent analyzes user queries, creates sub-agents, and oversees their independent exploration of different aspects of the query [19][21]. Performance Evaluation - Traditional evaluation methods are inadequate for multi-agent systems due to their non-linear and varied paths to achieving results; flexible evaluation methods are necessary [44][45]. - Anthropic employs a "LLM-as-judge" approach for evaluating outputs, which enhances scalability and practicality in assessing the performance of multi-agent systems [49][53]. Engineering Challenges - The complexity of maintaining state in intelligent agent systems poses significant engineering challenges, as minor changes can lead to substantial behavioral shifts [56][61]. - Anthropic has implemented robust debugging and tracking mechanisms to diagnose and address failures in real-time [57]. Conclusion - Despite the challenges, multi-agent systems have shown immense potential in open-ended research tasks, provided they are designed with careful engineering, thorough testing, and a deep understanding of current AI capabilities [61].

Core Insights - The emergence of DeepSeek, a domestic open-source AI model, has sparked discussions due to its free advantages, yet many still prefer to pay for ChatGPT, raising questions about user preferences and the quality of AI outputs [1][60]. - The output quality of AI tools is significantly influenced by user interaction, with 70% of the output quality depending on how users design their prompts [4][25]. Technical Differences - DeepSeek utilizes a mixed expert model with a training cost of $5.5 million, making it a cost-effective alternative compared to ChatGPT, which has training costs in the hundreds of millions [2]. - In the Chatbot Arena test, DeepSeek ranked third, demonstrating competitive performance, particularly excelling in mathematical reasoning with a 97.3% accuracy rate in the MATH-500 test [2]. Performance in Specific Scenarios - DeepSeek has shown superior performance in detailed analyses and creative writing tasks, providing comprehensive insights and deep thinking capabilities [3][17]. - The model's reasoning process is more transparent but requires structured prompts for optimal use, indicating that user guidance is crucial for maximizing its potential [7][12]. Cost and Efficiency - DeepSeek's pricing is 30% lower than ChatGPT, with a processing efficiency that is 20% higher and energy consumption reduced by 25% [8][9]. - For enterprises, private deployment of DeepSeek can be cost-effective in the long run, with a one-time server investment of around $200,000, avoiding ongoing API fees [9][10]. Deployment Flexibility - DeepSeek offers flexibility in deployment, allowing individual developers to run the 7B model on standard hardware, while enterprise setups can support high concurrency [11][10]. - The model's ability to run on lightweight devices significantly lowers the barrier for AI application [11]. Advanced Prompting Techniques - Mastery of advanced prompting techniques, such as "prompt chaining" and "reverse thinking," can significantly enhance the effectiveness of DeepSeek [13][14]. - The model's performance can be optimized by using multi-role prompts, allowing it to balance professionalism and readability [15][16]. Language Processing Capabilities - DeepSeek demonstrates a 92.7% accuracy rate in Chinese semantic understanding, surpassing ChatGPT's 89.3%, and supports classical literature analysis and dialect recognition [17]. Industry Applications - In finance, DeepSeek has improved investment decision efficiency by 40% for a securities company [18]. - In the medical field, it has achieved an 85% accuracy rate in disease diagnosis, nearing the level of professional doctors [19]. - For programming assistance, DeepSeek's error rate is 23% lower than GPT-4.5, with a 40% faster response time [20]. Complementary Nature of AI Tools - DeepSeek and ChatGPT are not mutually exclusive but serve as complementary tools, each suited for different tasks based on user needs [21][22]. - DeepSeek is preferable for deep reasoning, specialized knowledge, and data privacy, while ChatGPT excels in multi-modal interaction and creative content generation [24][22]. Importance of Prompting Skills - The ability to design effective prompts is becoming a core competency in the AI era, influencing the quality of AI outputs [54][55]. - The book "DeepSeek Application Advanced Tutorial" aims to enhance users' prompting skills and unlock the model's full potential [61].

近两年，随着AI的火热发展，"提示词（prompt）"这个词也被普通人熟知。 在AI短视频博主那里，这是AI时代的普通人必须要掌握的一项技能，"谁不会用提示词，谁就会被AI淘汰！"在焦虑的打工人那里，提示词是用AI来帮忙 完成工作的手段，需要整天琢磨对AI说什么才能得到更好的效果。这种焦虑也催生了众多"提示词工程"的知识付费课程，在AI还没真正落地之前，就先让 一帮嗅觉敏锐的人大赚一笔。 提示词也曾是许多没有AI和相关技术背景的人，想追赶AI风口的一条捷径。作为一种新职业，"提示词工程师"曾被许多人追捧，门槛低、上手快、薪资 高，成为转行AI的首选。"2023年的时候阿猫阿狗都能进来，挺好混的，挺水的。"从业者杨佩骏说。那时在国外有的提示词工程师甚至能拿到25-33万美 元年薪。 但现在，随着大模型能力的快速提升，提示词工程师越来越没有存在感，杨佩骏发现，辛辛苦苦优化了很长时间的提示词，模型一升级，就相当于白干 了。模型理解自然语言、推理与思考能力越来越强，传统意义上只会写提示词的提示词工程师已经失去竞争力，AI、模型公司们也不愿意招了。 "现在大家稍微有一点职业追求，都不愿意承认自己是PE（prompt e ...

提示词工程师 ，不用写代码、不限专业、不要求学历，只需研究如何和AI聊天，就能在2023年拿到25-33万美元年薪。 但如今，它已经沦为企业最不愿意扩增的岗位之一。 微软一项涉及31000名员工的调查显示，提示词工程师已经成为公司未来12-18个月内 倒数第二 不想新增的岗位。 同时在招聘平台（Indeed）上，提示词工程师的检索次数也在经历了过山车式变化。 要知道，当年这一新岗位可是得到了OpenAI奥特曼和AI大神卡帕西的共同认可。 明敏 发自 凹非寺 量子位 | 公众号 QbitAI 大模型元年最热门的AI岗位，现在已经过气了—— 2年时间过去，懂提示词工程确实依旧是项技能，但衍生出的岗位却已经不那么刚需了。 搞AI课程培训的高管表示： 无论你是财务、HR还是法务， 懂提示词工程已经是一种基本的职业技能 ，而不是需要再专门招一个岗位。 提示工程已成基本必备技能 梳理现状，提示词工程领域现在呈现出三个新趋势： 1、AI可以自动化提示词工程 2、普通人上手门槛变低 3、企业需要更加复合型人才 最初，提示词工程师的工作内容被定义为"用合适的描述让AI发挥出最大的潜力"。 最早一批开设该岗位的AI公司包括Ant ...

AI浪潮已至，DeepSeek正在重塑着我们的工作方式，效率才是王道！为了帮助大家快速掌握AI、提高工作 效率，我们整理了全套入门指南+高阶技巧，免费分享给大家： 免费领取 Deepseek 精选学习资料 共含 35份 领取方式 北京大学 《《 DDeeeeppSSeeeekk 提提示示词词工工程程和和落落地地场场景景》》 扫码添加梧桐小师弟 免费领取35份DeepSeek资料 1.DeepSeek提示词技巧-真诚+直接 传统 你现在是一个新能源汽车的市场研究 分析师,这里有一份调研报告总结需 要写成周报,请按周报的格式帮我完 成并进行润色,不少于500字。 DeepSeek (真诚是必杀技) 18 2.DeepSeek提示词技巧-通用公式 W 做什么 给谁用 음 E T 担心的问题 "内心戏" 20 3.DeepSeek提示词技巧-说人话 适合场景:科研,了解新事物 了避免DeepSeek的回答过于官方、专业,可以尝试这三个字"说人话" 你问：什么是"波粒二象性",DeepSeek大概率会给出专业目看不懂的回答,和百度百科差不多。但如 果给ta一句"说人话",ta就会生动形象的做一些举例 免费了的老者的意志 ...