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 can lead to structure in relation to the task. In the context of natural language processing and LLMs, a prompt is an input provided to the model to generate a response or prediction. Prompt Engineering February number of tokens to generate in a response. Generating more tokens requires more computation from the LLM, leading to higher energy consumption, potentially slower response times, and higher costs. Prompt

Artificial Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational trends related to AI. A starting collection of several disparate extensive retraining to handle new problem domains – they would transfer learning and operate with context, much like human experts. Additionally, humanoid robots powered by AGI would have the power to adopt and govern it. *Inference = Fully-trained model generates predictions, answers, or content in response to user inputs. This phase is much faster and more efficient than training. Next Frontier For AI

to the user. 02 It has access to various tools to interact with external systems—both to gather context and to take actions—and dynamically selects the appropriate tools depending on the workflow’s current preset criteria. In contrast, an LLM agent functions more like a seasoned investigator, evaluating context, considering subtle patterns, and identifying suspicious activity even when clear-cut rules aren’t friction: 01 Complex 
 decision-making: Workflows involving nuanced judgment, exceptions, or 
 context-sensitive decisions, for example refund approval 
 in customer service workflows. 02 Difficult-to-maintain

tested the previous job matching engine against the GPT-powered version with the new, customized context. 
 The performance uplift was significant: A 20% increase in job applications started A 13% uplift Domain expertise Fine-tuned models better understand your industry’s terminology, style, and context. Consistent tone and style For a retailer, that could mean every product description stays true Our support teams were getting bogged down, spending time accessing systems, trying to understand context, craft responses, and take the right actions for customers. So we built an internal automation

inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention Infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.4 Long Context Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Evaluations . . . . equipped with a total of 236B parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. We optimize the attention modules and Feed-Forward Networks (FFNs) within

字数要求、段落结构、用词风格、 内容要点、输出格式… CO-STAR提示语框架 新加坡 GPT-4 提示工程竞赛冠军提示词框架 "R"，代表 "Response"， 想要的回应类型。 一份详细的研究 报告？一个表格？ Markdown格式？ "C"代表 “Context（上 下文）” 相关的 背景信息，比如 你自己或是你希 望它完成的任务 的信息。 "O"代表 “Objective （目标）” 明 确的指示告诉

including both gastropods and bivalves, show phenotypicplasticity in their shell morphology in response to predation risk (Appleton & Palmer1988, Trussell & Smith 2000, Bourdeau 2010). Predation can including both gastropods and bivalves, exhibit phenotypic plasticity in their shell morphology in response to predation risk. Predation can act as a directional selection pressure, resulting in specific

题，一段详细的指令，也可以是一个复杂的任务描述。 提示语的基本结构包括指令、上下文和期望 ▪ 指令（Instruction)：这是提示语的核心，明确告诉AI你希望 它执行什么任务。 ▪ 上下文（Context)：为AI提供背景信息，帮助它更准确地理 解和执行任务。 ▪ 期望（Expectation)：明确或隐含地表达你对AI输出的要求 和预期。 提示语类型 提示语的本质 1. 指令型提示语：直接告诉AI需要执行的任务。 成一个连贯的思维链条。这个过程可以将提示语链设 计成模块化的结构，使其易于调整和重用，提高提示 语链的灵活性和效率。 模块化提示语链设计 提示语链的设计模型 为了更好地理解和设计提示语链，可采用CIRS模型（Context, Instruction, Refinement, Synthesis）。这个 模型概括了提示语链设计的四个关键环节： R e f i n e m e n t （ 优 化 ） C o n t

题，一段详细的指令，也可以是一个复杂的任务描述。 提示语的基本结构包括指令、上下文和期望 ▪ 指令（Instruction)：这是提示语的核心，明确告诉AI你希望 它执行什么任务。 ▪ 上下文（Context)：为AI提供背景信息，帮助它更准确地理 解和执行任务。 ▪ 期望（Expectation)：明确或隐含地表达你对AI输出的要求 和预期。 提示语类型 提示语的本质 1. 指令型提示语：直接告诉AI需要执行的任务。 成一个连贯的思维链条。这个过程可以将提示语链设 计成模块化的结构，使其易于调整和重用，提高提示 语链的灵活性和效率。 模块化提示语链设计 提示语链的设计模型 为了更好地理解和设计提示语链，可采用CIRS模型（Context, Instruction, Refinement, Synthesis）。这个 模型概括了提示语链设计的四个关键环节： R e f i n e m e n t （ 优 化 ） C o n t

题，一段详细的指令，也可以是一个复杂的任务描述。 提示语的基本结构包括指令、上下文和期望 • 指令 (Instruction): 这是提示语的核心，明确告诉Al你希 望 它执行什么任务。 • 上下文 (Context): 为Al提供背景信息，帮助它更准确地理 解和执行任务 。 • 期望 (Expectation): 明确或隐含地表达你对Al输出的要求 和 预 期 。 指令 (任务描述)