What is GEO (Generative Engine Optimization)?

GEO is an optimization strategy for AI search engines. When users search through ChatGPT, Gemini, or Perplexity, these engines pull content from the web to answer questions. GEO makes your website more likely to be cited and recommended by AI.

How is GEO different from traditional SEO?

Traditional SEO optimizes for Google search rankings. GEO optimizes for whether AI search engines (ChatGPT, Gemini, Perplexity) will cite and recommend your website. They're complementary — GEO focuses more on structured data, content citability, and authority signals.

The GEO score and quick improvement tips are completely free, no sign-in required. hogiah runs on a monthly subscription (Starter NT$1,080 / Growth NT$3,280 / Pro NT$12,800) that includes continuous AI citation testing, detailed 6-dimension analysis, fix suggestions, and keyword recommendations.

What is AEO (Answer Engine Optimization)?

AEO (Answer Engine Optimization) is the practice of optimizing your content to appear as direct answers in AI-powered search engines and answer engines. While GEO covers the broader strategy of visibility across generative AI platforms, AEO specifically targets how well your content serves as a definitive answer when users ask questions through ChatGPT, Perplexity, Google AI Overview, and similar tools. AEO matters because AI engines increasingly provide direct answers instead of links — if your content isn't structured as a clear, authoritative answer, it won't be selected. Key AEO tactics include FAQ schema markup, concise answer-first formatting, entity optimization, and building topical authority.

How do I check if my website appears in ChatGPT search results?

Manually checking your visibility in ChatGPT is unreliable because AI responses vary by session, user context, and model version. There's no fixed ranking page you can check like Google search results. hogiah solves this by running automated, systematic queries across ChatGPT, Perplexity, Gemini, and Google AI Overview to track whether your brand or website is being cited. The platform monitors your visibility over time, detects changes in citation patterns, and provides actionable reports so you know exactly where you stand across all major AI search engines.

What is the difference between GEO and AEO?

GEO (Generative Engine Optimization) is the broad discipline of optimizing content for all generative AI platforms — it encompasses technical optimization, content strategy, and visibility tracking across AI search engines. AEO (Answer Engine Optimization) is a subset focused specifically on making your content the preferred answer when users ask direct questions. Think of it this way: GEO ensures your brand is visible and cited across AI platforms, while AEO ensures your specific content is selected as the authoritative answer to relevant queries. Both are essential — GEO builds your overall AI presence, and AEO captures high-intent question-based traffic.

How can I improve my website's visibility in AI search engines?

Improving AI search visibility requires a multi-faceted approach. First, implement structured data (JSON-LD schema markup) so AI engines can parse your content programmatically. Second, add comprehensive FAQ schemas that directly answer common questions in your industry. Third, write authoritative, citation-worthy content with clear claims backed by data. Fourth, optimize for entities — ensure your brand, products, and key concepts are clearly defined. Fifth, build topical authority by covering subjects comprehensively rather than superficially. Finally, monitor your AI visibility using tools like hogiah to track which engines cite you and identify gaps. Regular content updates and fresh data also signal relevance to AI crawlers.

What AI search engines should businesses monitor?

Businesses should monitor at least five major AI search platforms: ChatGPT (the largest user base for AI-powered search), Google AI Overview (integrated directly into Google search results, affecting billions of queries), Perplexity (growing rapidly as a dedicated AI search engine with real-time citations), Gemini (Google's AI assistant with deep integration into Google's ecosystem), and Claude (Anthropic's AI with a strong user base in professional and research contexts). Multi-engine monitoring is critical because each AI platform uses different models, training data, and citation algorithms — your brand might be well-cited on Perplexity but invisible on ChatGPT. hogiah tracks all of these simultaneously to give you a complete picture.

Does structured data help with AI search visibility?

Yes, structured data is one of the most impactful factors for AI search visibility. JSON-LD schema markup makes your content machine-readable, allowing AI engines to accurately parse your business information, products, services, FAQs, and expertise. Specifically, FAQ schema helps your answers appear in AI-generated responses, Organization schema establishes your brand entity, Product schema ensures accurate product information, and Article schema helps AI engines understand your content's authority and recency. Without structured data, AI engines must infer meaning from unstructured HTML, which often leads to misinterpretation or omission. hogiah's GEO analysis includes a dedicated structured data dimension that audits your schema markup and provides ready-to-use code fixes.

What is Google AI Overview and how does it affect SEO?

Google AI Overview (formerly SGE — Search Generative Experience) is Google's AI-powered summary that appears at the top of search results for many queries. Instead of showing only links, Google now generates a synthesized answer using AI, pulling information from multiple web sources. This fundamentally changes SEO because even if your site ranks #1 organically, users may get their answer from the AI Overview without clicking through. To maintain visibility, businesses need to optimize for both traditional rankings AND AI Overview inclusion. This means creating content that AI can easily cite — clear, factual, well-structured information with strong E-E-A-T signals. hogiah tracks whether your content appears in Google AI Overview responses alongside traditional AI search engines.

TutorialJune 1, 20268 min read

What Is llms.txt? Help AI Engines Discover Your Brand

A complete guide to llms.txt — the emerging standard that helps AI language models understand your brand. Learn the spec, see real examples, and implement it for better GEO performance.

What Is llms.txt?

llms.txt is a proposed web standard created by Jeremy Howard of Answer.AI in 2024. It is a plain-text Markdown file placed in your website's root directory that serves as a navigation guide for AI language models, helping them quickly find your most important content.

Unlike search engines that systematically crawl and index entire websites, LLMs fetch information in real-time when users ask questions. They do not permanently index or "remember" your site structure. llms.txt gives AI a concise map of your highest-value pages.

How It Differs from robots.txt and sitemap.xml

Aspect	llms.txt	robots.txt	sitemap.xml
Purpose	Guides AI to key content	Controls crawler access	Lists all URLs for indexing
Audience	AI language models	Search engine crawlers	Search engine crawlers
Format	Markdown	Plain text directives	XML
Can block access	No	Yes	No

Critically, llms.txt contains no directives. It cannot grant or deny access -- it purely guides AI to your most valuable pages.

Format Specification

Per the official spec, the only required element is an H1 heading with your site name. Recommended elements include a blockquote summary, detailed information sections, and link lists organized under H2 headings using the format [Name](URL): Description.

A special ## Optional section marks secondary resources that can be skipped when shorter context is needed.

Example: hogiah's llms.txt

As an early adopter, hogiah maintains its own llms.txt. Here is a simplified version:

# hogiah

> hogiah is built for Taiwan's Traditional Chinese market, tracking brand visibility across 6 standard AI engines: ChatGPT, Perplexity, Google AI Overview, Gemini, Claude, and Grok.

## Product
- [AI Visibility Monitoring](/v2-preview/features/visibility): Track brand mentions across 6 standard AI engines (Starter 1 / Growth 3 / Pro 6)
- [Citation Analysis](/v2-preview/features/citations): Analyze AI citation sources and trends

## Resources
- [What is GEO?](/blog/what-is-geo): Complete guide to Generative Engine Optimization
- [GEO vs SEO vs AEO](/blog/geo-vs-seo-vs-aeo): Comparison of search optimization strategies

Implementation Steps

Create a file named llms.txt (case-sensitive) in UTF-8 encoding
Write Markdown content with 15-30 curated high-value pages
Upload to your root directory at https://yourdomain.com/llms.txt
Verify: HTTP 200 status, text/plain content type, HTTPS protocol
Review quarterly or after major site updates

Current AI Engine Support (Mid-2026)

OpenAI's GPTBot, Microsoft's crawlers, and Anthropic's ClaudeBot have been confirmed to fetch llms.txt files. However, no major AI company has publicly committed to using it in production systems. Research shows no statistically significant correlation with AI citation frequency yet.

Despite this, implementation takes under 30 minutes and positions your brand for future AI discoverability. Combined with hogiah's visibility monitoring, you can track whether llms.txt impacts your brand's AI mention rate over time.

FAQ

Will llms.txt replace robots.txt? No. They serve entirely different purposes and your site should have both.

Will AI cite me more after adding llms.txt? Not immediately. Think of it as a long-term investment in AI discoverability.

Can I write llms.txt in Chinese? Yes. AI models support multilingual understanding. Write in whichever language serves your target market.

How large should the file be? Under 5,000 tokens (about 15-30 curated links). For extensive documentation, create an additional llms-full.txt file.

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