常闻以"知识审校"为核心能力，结合大模型技术与权威知识库，为用户提供一套完整的内容生产与质量控制系统。如果你需要写得正确、写得合规、写得 符合事实，常闻写作助手就是为你设计的高标准内容生产工具。立即申请试用吧！ 核心能力——知识审校，不只是改错字，而是校事实 市面上大多数写作工具，只能解决：错别字、语法问题、基础表达不通顺，但在真实工作中，真正棘手的问题是： 事实是否准确？ 概念是否混用？ 表述是否规范、合规？ 专业术语是否使用正确？ 基于常闻科技积累的海量权威数据资源，产品构建了多领域专业知识库，并与大模型进行深度融合，使系统能够从事实、知识与规范层面对文本进行审 校，而不仅是语言层面的检查。 一句话总结：别人帮你检查"字对不对"，常闻写作助手帮你检查"内容对不对"。 功能一览 智能生成： 输入核心观点或数据，瞬间生成高质量的新闻通稿、工作汇报、调研报告。 多格式支持： 无论是简短的社交媒体文案，还是长达百页的深度白皮书，均可一键生成初稿，大幅缩短起草时间。 图书出版 痛点： "三审三校"流程繁琐，人力成本高，且难以发现专业性硬伤。 常闻方案： 充当"初审+质检"双重角色。在编辑介入前完成第一轮深度清洗，标 ...

Core Insights - The report highlights the emergence of AI infrastructure software (AI Infra) as a critical enabler for the deployment of generative AI applications, marking a golden development period for infrastructure software [1] - Unlike the model training phase dominated by tech giants, the inference and application deployment stages present new commercial opportunities for independent software vendors [1] - Key products in this space include computing scheduling software and data-related software, with computing scheduling capabilities directly impacting the profitability of model inference services [1][2] Computing Scheduling - AI Infra is designed to efficiently manage and optimize AI workloads, focusing on large-scale training and inference tasks [2] - Cost control is crucial in the context of a price war among domestic models, with Deepseek V3 pricing significantly lower than overseas counterparts [5] - Major companies like Huawei and Alibaba have developed advanced computing scheduling platforms that enhance resource utilization and reduce GPU requirements significantly [5][6] - For instance, Huawei's Flex:ai improves utilization by 30%, while Alibaba's Aegaeon reduces GPU usage by 82% through token-level dynamic scheduling [5][6] Profitability Analysis - The report indicates that optimizing computing scheduling can serve as a hidden lever for improving gross margins, with a potential increase from 52% to 80% in gross margin by enhancing single-card throughput [6] - The sensitivity analysis shows that a 10% improvement in throughput can lead to a gross margin increase of 2-7 percentage points [6] Vector Databases - The rise of RAG (Retrieval-Augmented Generation) technology has made vector databases a necessity for enterprises, with Gartner predicting a 68% adoption rate by 2025 [10] - Vector databases are essential for supporting high-speed retrieval of massive datasets, which is critical for RAG applications [10] - The demand for vector databases is expected to surge, driven by a tenfold increase in token consumption from API integrations with large models [11] Database Landscape - The data architecture is shifting from "analysis-first" to "real-time operations + analysis collaboration," emphasizing the need for low-latency processing [12][15] - MongoDB is positioned well in the market due to its low entry barriers and adaptability to unstructured data, with significant revenue growth projected [16] - Snowflake and Databricks are expanding their offerings to include full-stack tools, with both companies reporting substantial revenue growth and customer retention rates [17] Storage Architecture - The transition to real-time AI inference is reshaping storage architecture, with a focus on reducing IO latency [18] - NVIDIA's SCADA solution demonstrates significant improvements in IO scheduling efficiency, highlighting the importance of storage performance in AI applications [18][19]

RAG技术远非简单的数据注入，而是重塑AI理解与决策的核心框架。本文深度拆解RAG项 目中的真实困境——从语料筛选、矛盾处理到结果交付，揭示为何90%的工作仍依赖人类判 断。 在之前的文章里，我花了很多篇幅讲 RAG 为什么重要。但真正走到项目现场，你会很快意识到一件 事：RAG 不是一个"加模块"的技术问题，而是一整套数据与判断体系。 很多刚接触的人会以为，RAG 项目无非就是： 给模型多喂点资料，让它照着说。 但真实情况是——真正决定 RAG 效果的，从来不是"有没有资料"，而是"资料怎么被用"。 先从一个最真实的工作场景说起 在对话式 AI 助手场景中，RAG 项目面对的，通常不是"标准问答"，而是这样一种结构： 模型要做的，不是简单复述材料，而是： 理解对话语境 → 判断哪些材料有用 → 整合信息 → 给出一个"对用户有帮助"的回答 从训练视角看，这本质是在做一件事：材料阅读理解 + 问题理解 + 信息整合 + 表达控制 RAG 项目里的"三件套"：问题、材料、回答 如果把一个 RAG 项目拆开来看，它其实由三块内容构成，但这三块，没有一块是"天然可靠"的。 问题，本身就可能有问题 你在项目中会频繁遇 ...

01、通用模型分类：聘请一位"聪明的临时工" 这种方式特别适合那些偶发的、数据量不算特别大的"临时任务"，或者是项目初期还在探索确立分类标准的阶段。它的操作逻辑非常直观：你可以先将一 部分用户反馈连同项目背景一起扔给 AI，让它基于语意分析自动总结出合适的分类标签；待分类标准定好后，再命令它依照这些标签批量给反馈进行打 标。这就好比你请了一位理解力极强的"临时工"，不仅能帮你制定分类规则，还能迅速执行繁琐的分拣任务。 这种做法的优势在于具备良好的灵活性和便捷性，你无需提前准备复杂的训练数据，甚至当发现标签不合适时，只需一句话指令即可随时增减调整，完全 不需要重新训练模型。而且它的使用门槛低，利用市面上常见的对话式 AI 工具（如豆包、Deepseek、Kimi等）即可上手；若数据量庞大，只需对接一个 API 接口便能实现飞一般的处理效率。当然，这位"临时工"也有局限，当面对复杂度较高或涉及深奥行业术语的分类任务时，它可能会因为缺乏专业背景 而出现判断偏差。 提效小贴士： 想要这位"临时工"干活漂亮，你给它的指令（提示词）很关键！ 02、SFT微调模型分类：专业场景的"定制专家" 数字化时代，用户研究正迎来前所 ...

Core Insights - The article highlights the challenges faced by customer service teams, including high workload and inefficiencies in handling inquiries [2] - It introduces DeepSeek and ChatWiki as a solution for building an efficient AI customer service system without the need for complex development [2] Group 1: AI Customer Service Solution - DeepSeek's strong semantic understanding captures customer intent accurately, while ChatWiki builds a private knowledge base using RAG technology, ensuring responses are both warm and professional [2] - The entire process is zero-code, allowing deployment within one day, significantly lowering technical barriers and time costs for businesses [2] Group 2: Integration and Setup - ChatWiki is compatible with over 20 mainstream AI models, enabling easy integration for businesses without requiring specialized developers [3] - The knowledge base can be built by uploading various document formats, with ChatWiki handling text cleaning and conversion automatically [4] Group 3: AI Bot Creation - After setting up the knowledge base, businesses can create a personalized AI bot by configuring its name and welcome message, linking it to the knowledge base for immediate deployment [6] - DeepSeek extracts relevant information from the knowledge base to provide coherent responses, improving the quality of customer interactions [6] Group 4: Multi-Channel Support - The AI bot can be integrated across multiple platforms, including H5 links, company websites, and messaging apps, ensuring a consistent service experience for customers [8] - An education platform reported a 100% response rate for nighttime inquiries and doubled conversion rates after integration, demonstrating the commercial value of the solution [8] Group 5: Role Management - ChatWiki offers detailed permission management, allowing administrators to assign roles and control access to knowledge base editing and bot configuration, enhancing data security and team collaboration [10] - This feature supports complex organizational structures while ensuring the safety of core business data [10]

Core Insights - The Western Airport Group is advancing its smart airport construction strategy centered around a 4A framework, integrating AI technologies to enhance operational efficiency and decision-making capabilities [1][2] - The implementation of the "AI + Special Action" initiative, particularly through the enterprise-level AI assistant "Xiao Xi," is a significant step towards the digital transformation of the Northwest airport cluster [1][3] Group 1: AI Integration and Operational Efficiency - The weak current center at Yinchuan Airport has developed a platform combining "AI Intelligent Body + Local RAG Knowledge Base," which addresses inefficiencies in data retrieval and knowledge management [1] - Traditional document management has been inadequate for the fast-paced aviation industry, prompting the adoption of a "RAG + Local Large Model" approach to ensure data security and timely access to critical information [1][2] Group 2: Technical Achievements and Benefits - The local knowledge base built by the weak current center utilizes RagFlow and integrates DeepSeek large models, achieving a localized "retrieve-infer-generate" process that aligns with the group's principles of proprietary data and value exclusivity [2] - The AI assistant can generate visual reports with risk assessments and recommendations in seconds, significantly improving the efficiency of flight guarantee operations [2] Group 3: Broader Implementation and Future Prospects - The pilot results from the weak current center are being expanded across all departments, introducing tools for rapid regulatory compliance checks and proactive risk management [3] - The platform is designed for future integration with live data on flight dynamics, passenger services, and energy consumption, aiming to enhance predictive decision-making capabilities within the smart civil aviation system [3]

Core Viewpoint - The article discusses various mainstream AI Agent frameworks, highlighting their unique features and suitable application scenarios, emphasizing the growing importance of AI in automating complex tasks and enhancing collaboration among agents [1]. Group 1: Mainstream AI Agent Frameworks - Current mainstream AI Agent frameworks are diverse, each focusing on different aspects and applicable to various scenarios [1]. - The frameworks discussed include LangGraph, AutoGen, CrewAI, Smolagents, and RagFlow, each with distinct characteristics and use cases [1][2]. Group 2: CrewAI - CrewAI is an open-source multi-agent coordination framework that allows autonomous AI agents to collaborate as a cohesive team to complete tasks [3]. - Key features of CrewAI include: - Independent architecture, fully self-developed without reliance on existing frameworks [4]. - High-performance design focusing on speed and resource efficiency [4]. - Deep customizability, supporting both macro workflows and micro behaviors [4]. - Applicability across various scenarios, from simple tasks to complex enterprise automation needs [4][7]. Group 3: LangGraph - LangGraph, created by LangChain, is an open-source AI agent framework designed for building, deploying, and managing complex generative AI agent workflows [26]. - It utilizes a graph-based architecture to model and manage the complex relationships between components in AI workflows [28]. Group 4: AutoGen - AutoGen is an open-source framework from Microsoft for building agents that collaborate through dialogue to complete tasks [44]. - It simplifies AI development and research, supporting various large language models (LLMs) and advanced multi-agent design patterns [46]. - Core features include: - Support for agent-to-agent dialogue and human-machine collaboration [49]. - A unified interface for standardizing interactions [49][50]. Group 5: Smolagents - Smolagents is an open-source Python library from Hugging Face aimed at simplifying the development and execution of agents with minimal code [67]. - It supports various functionalities, including code execution and tool invocation, while being model-agnostic and easily extensible [70]. Group 6: RagFlow - RagFlow is an end-to-end RAG solution focused on deep document understanding, addressing challenges in data processing and answer generation [75]. - It supports various document formats and intelligently identifies document structures to ensure high-quality data input [77][78]. Group 7: Summary of Frameworks - Each AI Agent framework has unique characteristics and suitable application scenarios: - CrewAI is ideal for multi-agent collaboration and complex task automation [80]. - LangGraph is suited for state-driven multi-step task orchestration [81]. - AutoGen is designed for dynamic dialogue processes and research tasks [86]. - Smolagents is best for lightweight development and rapid prototyping [86]. - RagFlow excels in document parsing and multi-modal data processing [86].

Core Insights - The report evaluates the technical capabilities of four major AI model development platforms: Alibaba Cloud's Bailian, Tencent Cloud's Intelligent Agent Development Platform, Kouzi, and Baidu Intelligent Cloud Qianfan, focusing on RAG capabilities, workflow capabilities, and agent capabilities [1][7][8]. RAG Capability Testing - RAG capability testing assesses knowledge enhancement mechanisms, including multi-modal knowledge processing, task complexity adaptation, and interaction mechanism completeness [7][8]. - In text question answering, all platforms demonstrated high accuracy, with over 80% accuracy in multi-document responses, although some platforms showed stability issues during API calls [20][21]. - Baidu Intelligent Cloud Qianfan exhibited stable performance in complex query scenarios for structured data, while Tencent Cloud achieved 100% refusal for out-of-knowledge-base questions [21][23]. - The platforms showed differences in handling refusal and clarification, with Tencent Cloud providing 100% refusals for non-knowledge-base questions [21][22]. Workflow Capability Testing - Workflow capability testing focuses on dynamic parameter extraction, exception rollback, intent recognition, and fault tolerance [35][36]. - The end-to-end accuracy for workflow processes ranged from 61.5% to 93.3%, with Tencent Cloud leading in intent recognition accuracy at 100% [36][37]. - The platforms demonstrated basic usability in workflow systems, but there is room for improvement in complex information processing [38][39]. Agent Capability Testing - Agent capability testing evaluates the ability to call tools, focusing on intent understanding, operational coordination, feedback effectiveness, and mechanism completeness [44][45]. - All platforms achieved high single-tool call completion rates (83%-92%), but multi-tool collaboration and prompt calling showed potential for improvement [48][50]. - Tencent Cloud's Intelligent Agent Development Platform excelled in tool call success rates due to its robust ecosystem and process optimization [49][50].

Core Viewpoint - The discussion around VLA (Vision-Language-Action model) is intensifying, with contrasting opinions on its short-term feasibility and potential impact on the automotive industry [2][12]. Group 1: VLA Technology and Development - The Li Auto i8 is the first vehicle to feature the VLA driver model, positioning it as a key selling point [2]. - Bosch's president for intelligent driving in China, Wu Yongqiao, expressed skepticism about the short-term implementation of VLA, citing challenges in multi-modal data acquisition and training [2][12]. - VLA is seen as an "intelligent enhanced version" of end-to-end systems, aiming for a more human-like driving experience [2][5]. Group 2: Comparison of Driving Technologies - There are two main types of end-to-end technology: modular end-to-end and one-stage end-to-end, with the latter being more advanced and efficient [3][4]. - The one-stage end-to-end model simplifies the process by directly mapping sensor data to control commands, reducing information loss between modules [3][4]. - VLA is expected to outperform traditional end-to-end models by integrating multi-modal capabilities and enhancing decision-making in complex scenarios [5][6]. Group 3: Challenges and Requirements for VLA - The successful implementation of VLA relies on breakthroughs in three key areas: cross-modal feature alignment, world model construction, and dynamic knowledge base integration [7][8]. - Current automotive chips are not designed for AI large models, leading to performance limitations in real-time decision-making [9][11]. - The industry is experiencing a "chip power battle," with companies like Tesla and Li Auto developing their own high-performance AI chips to meet VLA's requirements [11][12]. Group 4: Future Outlook and Timeline - Some industry experts believe 2025 could be a pivotal year for VLA technology, while others suggest it may take 3-5 years for widespread adoption [12][13]. - Initial applications of VLA are expected to be in controlled environments, with broader capabilities emerging as chip technology advances [14]. - Long-term projections indicate that advancements in AI chip technology and multi-modal alignment could lead to significant breakthroughs in VLA deployment by 2030 [14][15].

Core Viewpoint - The essence of AI Agents lies in reconstructing the "cognition-action" loop, iterating on human cognitive processes to enhance decision-making and execution capabilities [1][4][41]. Group 1: Breakthroughs in AI Agents - The breakthrough of large language models (LLMs) is fundamentally about decoding human language, enabling machines to possess near-human semantic reasoning abilities [2]. - AI Agents transform static "knowledge storage" into dynamic "cognitive processes," allowing for more effective problem-solving [4][7]. - The memory system in AI Agents plays a crucial role, with short-term memory handling real-time context and long-term memory encoding user preferences and business rules [10][12][13]. Group 2: Memory and Learning Capabilities - The dual memory mechanism allows AI Agents to accumulate experience, evolving from passive tools to active cognitive entities capable of learning from past tasks [14][15]. - For instance, in customer complaint handling, AI Agents can remember effective solutions for specific complaints, optimizing future responses [15]. Group 3: Tool Utilization - The ability to call tools is essential for AI Agents to expand their cognitive boundaries, enabling them to access real-time data and perform complex tasks [17][20]. - In finance, AI Agents can utilize APIs to gather market data and provide precise investment advice, overcoming the limitations of LLMs [21][22]. - The diversity of tools allows AI Agents to adapt to various tasks, enhancing their functionality and efficiency [26][27]. Group 4: Planning and Execution - The planning module of AI Agents addresses the "cognitive entropy" of complex tasks, enabling them to break down tasks into manageable components and monitor progress [28][30][32]. - After completing tasks, AI Agents can reflect on their planning and execution processes, continuously improving their efficiency and effectiveness [33][35]. Group 5: Impact on Business and Society - AI Agents are redefining the underlying logic of enterprise software, emphasizing collaboration between human intelligence and machine capabilities [36][37]. - The evolution from tools to cognitive entities signifies a major shift in how AI can enhance human productivity and decision-making [39][41]. - As AI technology advances, AI Agents are expected to play significant roles across various sectors, including healthcare and education, driving societal progress [44][45]. Group 6: Practical Applications and Community - The company has developed its own AI Agent and established an AI Innovation Institute to assist enterprises in effectively utilizing AI for cost reduction and efficiency improvement [46][48]. - The institute offers practical tools and methodologies derived from extensive real-world case studies, enabling businesses to integrate AI into their operations [51][58]. - Monthly collaborative learning sessions serve as a reflection mechanism, allowing participants to convert theoretical knowledge into actionable solutions [60][62].