you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process. Inadequate prompts to find the best prompt, optimizing prompt length, and evaluating a prompt’s writing style and structure in relation to the task. In the context of natural language processing and LLMs, a prompt is an model gets stuck in a cycle, repeatedly generating the same (filler) word, phrase, or sentence structure, often exacerbated by inappropriate temperature and top-k/ Prompt Engineering February 2025 13

report to life. And, to the many friends and technology builders who helped, directly or via your work, and are driving technology forward.• Seem Like Change Happening Faster Than Ever? Yes, It Is • AI Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before • AI & Work Evolution = Real + Rapid 3 1 2 3 4 5 6 7 8 9-51 52-128 129-152 153-247 248-298 299-307 Source: Sensor Tower (5/25) 5/23 4/25 Mobile App Monthly Active Users, MM Details on Page 315 AI & Work Evolution = Real + Rapid 8 USA IT Jobs – AI vs. Non-AI Details on Page 302 +448% -9% 1/18

transcendentals (exp, tanh, erf, etc) - very general technique, allows clean vectorization - Related work in Gibiansky (2017), Gray (2019), et al. Image from OpenAI- Add relay.nn.sparse_dense for block-sparse reinterpret to implement rational approximations in user space (~10 lines of Relay IR) - A few days of work - TVM sampling model running in 30us on single server CPU core - Beat hand-written, highly optimized code-generation techniques (TVM, Xbyak, etc) - Interesting new tradeoffs - how const are parameters? - structure specialization trades off icache/ dcache - also available today in FBGEMMPyTorch and TVM - Lots

affect thedynamics of a specific trophic level as well as thedynamics of the entire community structure. 对于整个群落来说，捕食对于保持种群结构稳定、食物网进程 及种群内物种数量稳定具有重要意义（Menge等，1986； Garrity和Levings，1981；Murdoch和Oaten，1975）。 1975）。 For the entire community, pedation is crucial formaintaining population structure stability, food webprocesses, and stable species numbers within thepopulation (Menge et al, 1986; Garrity and

1. TASTE框架 • Task (任务): 定义模型主要任务或生成内容。 • Audience (目标受众): 明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 Novelty: 要求结合最新的环境数据，提出新颖的观点和解 决方案。 示例 • Task: 写一篇关于数据隐私的重要性的简短博客文章。 • Audience: 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。

1. TASTE框架 • Task (任务): 定义模型主要任务或生成内容。 • Audience (目标受众): 明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 Novelty: 要求结合最新的环境数据，提出新颖的观点和解 决方案。 示例 • Task: 写一篇关于数据隐私的重要性的简短博客文章。 • Audience: 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。

< 8 “cpu” “gpu”© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data structure class SpecializedConditionNode : public Node { Array conditions; }; class OpImplementNode

AI%&'() • Manus !"#$%&'()*+,-./012345-6708,9):;<=>Manus ?@A+'BCDEFGHIJK,LMN OPQMR<"S>TUVWXY3 less structure more intelligence GZ[5\]^_`abcde_`fgchi_`jEc'k_` lm,no computer usecdeep researchccoding agent

AI in the Enterprise Lessons from seven frontier companiesContents A new way to work 3 Executive summary 5 Seven lessons for enterprise AI adoption Start with evals 6 Embed AI into your products EnterpriseA new way 
 to work As an AI research and deployment company, OpenAI prioritizes partnering with global companies because our models will increasingly do their best work with sophisticated, complex can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve a lot of rote work, ripe for automation. Aim high. Let’s drill down into each of these, with customer stories as examples

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5 Conclusion, Limitation, and Future Work 21 A Contributions and Acknowledgments 27 B DeepSeek-V2-Lite: A 16B Model Equipped with MLA and summarize the conclusion, deliberate on the current limitations of DeepSeek-V2, and outline our future work (Section 5). 2. Architecture By and large, DeepSeek-V2 is still in the Transformer architecture et al., 2023). Note that AGIEval includes both English and Chinese subsets. Following our previous work (DeepSeek-AI, 2024), we adopt perplexity-based evaluation for datasets including HellaSwag, PIQA,