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Google AI Search Optimization

谷歌AI搜索优化

Overview

概述

Use this skill to turn Google's public guidance on generative AI features in Search into practical recommendations, audits, content plans, and implementation tasks.
Anchor every recommendation in this model: Google AI Overviews and AI Mode are extensions of Google Search. They rely on Google's Search index, ranking, quality, crawling, and serving systems, with AI techniques such as retrieval-augmented generation and query fan-out layered on top.
Treat AEO and GEO as industry labels for search optimization work, not as separate Google systems. For Google Search, optimizing for generative AI search means optimizing for the overall Search experience.
使用本技能将谷歌关于搜索中生成式AI功能的公开指南转化为实用建议、审计方案、内容规划和实施任务。
所有建议都基于以下模型：谷歌AI Overviews和AI Mode是谷歌搜索的扩展功能。它们依赖谷歌的搜索索引、排名、质量、抓取和服务系统，并叠加了retrieval-augmented generation（检索增强生成）和query fan-out（查询扩散）等AI技术。
将AEO和GEO视为搜索优化工作的行业标签，而非独立的谷歌系统。对于谷歌搜索而言，针对生成式AI搜索进行优化意味着优化整体搜索体验。

Source Handling

来源处理

If the user asks for the latest guidance, current policy, exact wording, or source citations, verify against the official sources before answering. Start with 
references/google-ai-search-principles.md
for the source map and browse only official Google, Google Search Central, Google Help, Merchant Center, web.dev, or directly linked protocol pages unless the user asks for broader research.
Do not present this skill as a replacement for the live docs. Google Search guidance changes.
如果用户询问最新指南、当前政策、确切措辞或来源引用，请先对照官方来源验证后再作答。首先查看
references/google-ai-search-principles.md
获取来源映射，仅浏览谷歌官方、谷歌搜索中心、谷歌帮助、Merchant Center、web.dev或直接链接的协议页面，除非用户要求更广泛的研究。
请勿将本技能视为实时文档的替代方案。谷歌搜索指南会不断变化。

Workflow

工作流程

  1. Classify the request:
    • Explain: summarize how Google's generative AI Search features work.
    • Audit: evaluate a site, page, content plan, or SEO recommendation.
    • Plan: create a roadmap, content brief, technical backlog, or prioritized checklist.
    • Implement: modify site code, metadata, structured data, media markup, crawl controls, or content.
    • Myth-check: evaluate AEO/GEO claims, llms.txt, schema claims, AI rewriting, mentions, or chunking advice.
  2. Gather only the needed context:
    • For a site audit, inspect crawlability, rendered content, titles, headings, internal links, canonicalization, robots/noindex, sitemaps, page experience, media, structured data, and Search Console constraints if available.
    • For a content task, identify audience, purpose, firsthand expertise, unique value, trust signals, media opportunities, and whether the content would satisfy the visitor without another search.
    • For ecommerce or local tasks, inspect Merchant Center/product data, Product structured data, Business Profile/business details, support details, shipping/returns, reviews, and inventory or offer freshness where relevant.
    • For JavaScript sites, check whether important text, links, media, and structured data appear in rendered HTML and each meaningful screen has a URL.
  3. Apply the core recommendations:
    • Build crawlable, indexable, snippet-eligible pages that return successful HTTP status codes.
    • Create non-commodity, helpful, reliable, people-first content with unique experience or expertise.
    • Organize content for humans with descriptive titles, headings, links, URLs, and page structure.
    • Use semantic HTML where practical for readability, accessibility, and navigation; do not overstate perfect HTML validity as a requirement.
    • Add high-quality images and videos when they genuinely help the user, and optimize those assets for discovery.
    • Use structured data for eligible rich results and clearer content meaning, but not as a special AI ranking hack.
    • Keep product, business, local, and support information accurate in Google-facing systems.
    • Improve page experience across mobile, security, speed, layout stability, intrusive elements, and main-content clarity.
    • Monitor with Search Console and test with URL Inspection, Rich Results Test, and PageSpeed Insights when relevant.
  4. Explicitly reject unsupported shortcuts:
    • Do not recommend 
      llms.txt
      or special AI text files as required for Google AI Search visibility.
    • Do not require content "chunking" into tiny pages or fragments for AI systems.
    • Do not rewrite pages only to target AI systems, long-tail query variants, or query fan-out guesses.
    • Do not create many near-duplicate pages for every possible fan-out query.
    • Do not seek fake mentions, manufactured citations, or reputation signals.
    • Do not overfocus on schema.org markup as if there were AI-specific schema.
    • Do not claim that meeting requirements guarantees crawling, indexing, ranking, or inclusion in AI features.
    • Do not imply every page needs overt SEO work to succeed; prioritize the changes that improve user value, discovery, indexing, and understanding.
  5. Produce output that is useful in the user's context:
    • For audits, lead with prioritized findings, then fixes, rationale, and validation steps.
    • For strategy, split work into content, technical, media, ecommerce/local, and measurement tracks.
    • For content briefs, require original expertise, evidence, audience value, media support, and trust signals.
    • For code changes, make narrow edits that improve crawlability, metadata, structured data, accessibility, or page experience, then validate.
  1. 对请求进行分类：
    • 解释：概述谷歌生成式AI搜索功能的工作原理。
    • 审计：评估网站、页面、内容规划或SEO建议。
    • 规划：创建路线图、内容 brief、技术待办事项或优先级检查表。
    • 实施：修改网站代码、元数据、结构化数据、媒体标记、抓取控制或内容。
    • 辟谣：评估AEO/GEO主张、llms.txt、schema相关说法、AI改写、提及内容或内容分块建议。
  2. 仅收集所需上下文：
    • 进行网站审计时，若有可用信息，检查可抓取性、渲染内容、标题、标题标签、内部链接、规范标签、robots/noindex、站点地图、页面体验、媒体、结构化数据和Search Console限制。
    • 处理内容任务时，确定受众、目的、一手专业知识、独特价值、信任信号、媒体机会，以及内容是否能在无需进一步搜索的情况下满足访客需求。
    • 处理电商或本地业务任务时，检查Merchant Center/产品数据、Product结构化数据、Business Profile/企业详情、支持详情、配送/退货政策、评论，以及相关的库存或报价时效性。
    • 处理JavaScript网站时，检查重要文本、链接、媒体和结构化数据是否出现在渲染后的HTML中，以及每个有意义的页面是否有对应的URL。
  3. 应用核心建议：
    • 构建可抓取、可索引、符合摘要展示条件的页面，返回成功的HTTP状态码。
    • 创建非同质化、实用可靠、以人为本的内容，具备独特体验或专业知识。
    • 以用户为中心组织内容，使用描述性标题、标题标签、链接、URL和页面结构。
    • 实际使用语义HTML以提升可读性、可访问性和导航性；无需过度强调完美的HTML有效性作为必备要求。
    • 当图片和视频能切实帮助用户时添加高质量资源，并优化这些资产以提升发现率。
    • 对符合条件的富结果使用结构化数据，明确内容含义，但不要将其视为针对AI的特殊排名技巧。
    • 确保面向谷歌的系统中产品、企业、本地和支持信息的准确性。
    • 提升移动端、安全性、速度、布局稳定性、侵入性元素和主内容清晰度等方面的页面体验。
    • 相关时使用Search Console进行监控，并通过URL Inspection、Rich Results Test和PageSpeed Insights进行测试。
  4. 明确拒绝无依据的捷径：
    • 不推荐将
      llms.txt
      或特殊AI文本文件作为谷歌AI搜索可见性的必备要求。
    • 不要求为AI系统将内容“分块”为极小页面或片段。
    • 不建议仅为针对AI系统、长尾查询变体或查询扩散猜测而重写页面。
    • 不建议为每个可能的扩散查询创建大量近似重复页面。
    • 不建议寻求虚假提及、人造引用或虚假声誉信号。
    • 不建议过度关注schema.org标记，仿佛存在AI专属的schema。
    • 不声称满足要求就能保证抓取、索引、排名或纳入AI功能。
    • 不暗示每个页面都需要进行公开的SEO工作才能成功；优先选择能提升用户价值、发现率、索引和理解度的变更。
  5. 生成符合用户场景的实用输出：
    • 审计报告先列出优先级发现，再给出修复方案、理由和验证步骤。
    • 策略规划将工作分为内容、技术、媒体、电商/本地业务和度量跟踪几个方向。
    • 内容 brief要求包含原创专业知识、证据、受众价值、媒体支持和信任信号。
    • 代码修改应进行精准编辑，提升可抓取性、元数据、结构化数据、可访问性或页面体验，然后进行验证。

References

参考资料

Read 
references/google-ai-search-principles.md
when you need the underlying model, source map, recommendations, or myth-busting detail.
Read 
references/audit-playbook.md
when performing a site/page audit, building an implementation roadmap, or creating a repeatable checklist.
当你需要了解底层模型、来源映射、建议或辟谣细节时，请阅读
references/google-ai-search-principles.md
。
当进行网站/页面审计、构建实施路线图或创建可重复使用的检查表时，请阅读
references/audit-playbook.md
。