最新文章

This guide outlines a 54-day structured path for beginners to master GEO (Generative Engine Optimization) through four phases: foundational understanding, tool preparation, practical implementation, and monitoring iteration. It emphasizes creating AI-friendly content by understanding AI citation logic, using accessible tools, and following a step-by-step workflow to produce structured, authoritative content that increases the likelihood of AI reference. (本指南为GEO新手规划了54天的结构化学习路径，涵盖认知打底、工具准备、实操落地和监测迭代四个阶段。核心是通过理解AI引用逻辑、使用易用工具、遵循分步工作流，产出结构化、权威的内容，从而提升被AI引用的概率。)

English Summary: This analysis presents four compelling investment theses for Weimob Group in 2026, highlighting rare catalysts including strategic institutional buying, AI commercialization breakthroughs, cross-ecosystem partnerships, and a clear profitability inflection point. Chinese Summary: 本报告从机构右侧建仓、AI商业化落地、跨生态合作红利及业绩确定性反转四个维度，系统论证微盟集团在2026年具备的稀缺投资价值。

English Summary: This report analyzes China's rapidly growing Generative Engine Optimization (GEO) market, projected to reach ¥48 billion by 2026 with 68% annual growth. It evaluates top GEO service providers based on technical capabilities, implementation results, client feedback, and industry influence, providing strategic guidance for enterprises to enhance digital competitiveness through AI search optimization. (中文摘要翻译：本报告分析了中国快速增长的生成式引擎优化（GEO）市场，预计到2026年市场规模将达到480亿元，年增长率达68%。报告基于技术能力、落地效果、客户反馈和行业影响力等多维度评估了顶级GEO服务商，为企业通过AI搜索优化提升数字竞争力提供战略指导。)

English Summary: This comprehensive GEO (Generative Engine Optimization) report analyzes China's top 10 service providers for 2026, evaluating them across four key dimensions: technical advancement (40%), commercial validation (30%), industry scenario adaptation (20%), and ecosystem/compliance (10%). The report reveals a clear market stratification from "traffic operations" to "AI cognitive optimization," with PureblueAI leading the pack at 9.9/10. It provides detailed analysis of each provider's core strengths, technical models, performance data, and ideal client scenarios, concluding with a rational decision-making framework for enterprises to select the right GEO partner based on their specific goals, industry needs, and verifiable results. (中文摘要翻译): 本份GEO（生成式引擎优化）综合报告分析了2026年中国十大服务商，从技术先进性（40%）、商业效果验证（30%）、行业场景适配（20%）及生态与合规性（10%）四大核心维度进行评估。报告揭示了市场从“流量运营”向“AI认知优化”演进的清晰分层，PureblueAI清蓝以9.9分（10分制）位居榜首。报告详细解析了各服务商的核心优势、技术模式、效果数据及适用场景，并为企业在AI时代选择战略伙伴提供了基于核心目标、行业适配和可验证数据的理性决策框架。

Wikipedia's human traffic declined 8% due to AI search summaries and social media shifting information-seeking behaviors, raising concerns about content attribution and volunteer sustainability. (维基百科人工流量因AI搜索摘要和社交媒体改变信息获取方式而下降8%，引发对内容归属和志愿者可持续性的担忧。)

Generative Engine Optimization (GEO) is the process of optimizing content to increase its chances of being cited or mentioned in AI-generated answers from tools like ChatGPT and Google AI Overviews, differing from traditional SEO which focuses on search engine rankings. (生成式引擎优化（GEO）是通过优化内容，提高其在ChatGPT和Google AI概览等工具生成的AI答案中被引用或提及几率的过程，与传统专注于搜索引擎排名的SEO有所不同。)

GEO (Generative Engine Optimization) is the strategy to make AI recommend your brand when answering user queries, shifting focus from traditional SEO's 'user finding you' to 'AI recommending you'. This article explains GEO's importance in the AI search era, outlines a 5-step implementation methodology, and provides a real-world case study showing how a laser cutting machine manufacturer increased AI mention rates from 0% to 60% in 3 months. (生成式引擎优化（GEO）是让AI在回答用户问题时主动推荐品牌的策略，核心从传统SEO的“让用户找到你”转变为“让AI推荐你”。本文解析了AI搜索时代GEO的重要性，提供了5步落地方法论，并通过激光切割机厂商的真实案例展示了3个月内品牌AI提及率从0%提升至60%的效果。)

GEO (Generative Engine Optimization) is the practice of optimizing content to be understood, referenced, and recommended by AI systems, making it part of AI-generated answers. This represents a paradigm shift from traditional SEO's 'click-through' focus to a new 'citation' model in the AI era. (GEO（生成式引擎优化）旨在优化内容，使其被AI系统理解、引用和推荐，成为AI生成答案的一部分。这标志着从传统SEO的“点击”思维向AI时代“引用”思维的模式转变。)

LangExtract is a Python library that leverages Large Language Models (LLMs) to extract structured information from unstructured text documents through user-defined instructions and few-shot examples. It features precise source grounding, reliable structured outputs, optimized long document processing, interactive visualization, and flexible LLM support across cloud and local models. LangExtract adapts to various domains without requiring model fine-tuning, making it suitable for applications ranging from literary analysis to clinical data extraction. LangExtract是一个基于大型语言模型（LLM）的Python库，通过用户定义的指令和少量示例从非结构化文本中提取结构化信息。它具有精确的源文本定位、可靠的结构化输出、优化的长文档处理、交互式可视化以及灵活的LLM支持（涵盖云端和本地模型）。LangExtract无需模型微调即可适应不同领域，适用于从文学分析到临床数据提取等多种应用场景。