Content Strategy in AI era | 매거진에 참여하세요

The End of SEO, the Rise of GEO

For the past 20 years, online content strategy revolved around SEO (Search Engine Optimization).
The goal was simple: optimize keywords, build links, and refine metadata to rank higher on Google.

But in 2025, users increasingly turn to generative AI search tools like Perplexity, ChatGPT, and You.com.
Instead of browsing search result pages themselves, users receive answers assembled by AI.

This shift has given rise to GEO (Generative Engine Optimization), optimizing content specifically for generative AI engines.

Why GEO Matters

If SEO was about making your page readable by search engines,

GEO is about making your content included in AI-generated answers.

• - Limitation of SEO:

• Keyword stuffing and link-building have little influence on AI responses.

• - Goal of GEO:

• Ensure your content is contextually integrated into the datasets that AI models learn from and reference.

• - Key Difference:

• Search engines return URLs, while AI returns sentences. GEO is therefore more akin to “sentence-level optimization.”

Core GEO Strategies

1. Provide Structured Data
Use tables, JSON, or llms.txt files to make information easily readable by AI.

2. Ensure Source Credibility
AI prioritizes trustworthy domains and citable sources.

.gov or .edu and reputable media carry higher weight.

• Even corporate blogs can improve inclusion probability with expert attribution or academic citations.

3. Keep Content Updated
AI models struggle with real-time data.
Providing dynamic access via RSS feeds or APIs increases the chance your content is reflected in AI answers.

4. Optimize for Multimodal Use
Text is no longer enough:images, charts, and videos that reinforce explanations are more likely to be leveraged by AI in responses.

GEO in Practice

• Travel & Hospitality:

• Expedia and Booking.com use llms.txt to include hotel information in AI-generated responses.

• E-Commerce:

• Some Shopify stores expose product data via JSON-LD to ensure AI reflects prices and stock in answers.

• Media:

• Major news outlets are pursuing API-based GEO partnerships rather than restricting content access.

What is llms.txt then?

llms.txt is a guidance file websites can provide to generative AI (LLM) crawlers.

Purpose:

Specify which content AI can learn from or reference, and which should be excluded.
Location:

Placed in the website root directory, readable by LLM crawlers.
Role:

Rules for AI model training/reference.

Key Features:

• - Allow/Disallow Rules: Specify which paths AI can include or ignore.

• - Usage Conditions: Require source attribution or link inclusion.

• - License/Permission: Restrict usage (e.g., non-commercial only).

Example llms.txt

Structure:

# Default rules for all LLMs
User-agent: *
Disallow: /private
Allow: /blog
Allow: /public

# Specific LLM crawler rules
User-agent: OpenAI
Allow: /ai-content
Disallow: /drafts

# Metadata policies
Policy: attribution-required
Policy: non-commercial

Difference from robots.txt

Note: Not all AI providers (OpenAI, Anthropic, Google) officially adopt llms.txt yet.
However, platforms like Perplexity, You.com, and some emerging AI startups already support it, making it a proactive strategy today.

Limitations & Concerns

• 

  Bias: Companies investing heavily in GEO may skew AI responses.

• Transparency: Users cannot easily see which content AI prioritizes.

• Lack of Standards: Unlike SEO, GEO rules are not yet unified.

Looking Ahead

In the next five years, GEO is expected to become the default strategy for AI search:

• Emergence of GEO-focused agencies

• Widespread adoption of standardized files like llms.txt

• AI visibility will become the primary KPI for online marketing

GEO goes beyond technical optimization, it represents a new marketing paradigm in the generative AI era.

SEO is ending. GEO is here.
The focus is no longer on ranking pages, it’s on having your brand included within AI-generated answers.
GEO is the content survival strategy for the age of generative AI.