GEO - How to Increase Visibility of Your Content in Large Language Models (LLMs)

How to Increase Visibility of Your Content in Large Language Models (LLMs)

Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, and AI-powered search experiences like Google AI Overviews are rapidly changing how users discover content.

Unlike traditional search engines, LLMs do not simply rank pages by keywords. Instead, they prioritise clarity, authority, structure, freshness, and trust signals when selecting and summarising content.

This guide explains how publishers and affiliate marketers can adapt their content and sites to be more visible, referenceable, and trusted by LLMs.

1. Understand How LLMs Discover and Use Content

LLMs surface content in two main ways:

Training and fine-tuning data (historical, high-authority public content)
Retrieval systems (live or semi-live access to structured, trustworthy pages)

LLMs prefer content that is:

Easy to parse
Clearly structured
Factually consistent
Frequently referenced elsewhere
Maintained and up to date

Affiliate content can perform extremely well when it provides genuine informational value, not just promotional intent.

2. Create High-Quality, Evergreen, Informational Content

LLMs consistently prioritise:

Guides
Tutorials
Reviews
Comparisons
“Best of” and category explainers

Best practices for affiliates:

Focus on helping the user decide, not just click
Include rationale behind recommendations
Explain who a product is best for and who it’s not for
Add context that goes beyond manufacturer descriptions

Avoid:

Thin content
Rewritten specs
Pages dominated by ads or affiliate links above the fold

3. Structure Content for Machine Readability

LLMs extract meaning from structure, not just text.

Use predictable sections:

Overview
Features
Pros & cons
Use cases
Comparisons
Alternatives
FAQs

Add summary elements:

A short TL;DR or key takeaway section
Bullet lists and tables
Concise definitions near the top

This makes it easier for LLMs to understand the topic, extract summaries, and attribute information accurately.

4. Implement Structured Data (Schema Markup)

Structured data significantly improves how machines interpret your content.

Recommended schema types:

Product
Review
ItemList
FAQPage
HowTo
BreadcrumbList

For affiliate publishers:

Ensure schema reflects real editorial content, not just affiliate links
Include accurate pricing, availability, and merchant details
Keep schema consistent across pages

5. Publish FAQ and Natural-Language Q&A Content

LLMs are trained heavily on question-and-answer patterns.

How to optimise:

Add FAQs to product and category pages
Use natural language:
- “What is the best…”
- “Is X worth it?”
- “Which option is better for…”
Keep answers concise, factual, and neutral

These sections are often directly summarised by LLMs and used in AI search overviews.

6. Build Topical Authority (Not Just Traffic)

LLMs identify expertise patterns across your site.

They favour publishers who:

Consistently cover the same subject area
Go deep, not broad
Update content regularly

Affiliate publishers should:

Choose a clear niche or vertical
Build clusters of related content
Maintain “Best of” and comparison pages over time

Topical authority matters more than publishing volume.

7. Strengthen Entity Signals & Brand Attribution

LLMs rely on entity recognition — understanding who is publishing content and how trustworthy they are.

Key signals include:

Consistent publisher name and branding
Clear author bylines and bios
Mentions and backlinks from authoritative sites
Consistent metadata and schema

The clearer your publisher identity, the more likely LLMs are to trust your content, reference it accurately, and attribute recommendations to your brand or website.

8. Keep Content Fresh and Accurate

Modern LLM systems strongly prefer recent, well-maintained content.

Best practices:

Add visible “Last updated” timestamps
Refresh key pages quarterly or biannually
Update pricing, features, and availability
Revise comparisons when products change

Freshness improves AI summary inclusion, trust scoring, and user experience.

9. Optimise for AI-Powered Search Experiences

Google AI Overviews and Bing AI Search use hybrid signals:

Traditional SEO
Structured data
Semantic clarity
Summary-friendly content

To optimise:

Put key conclusions near the top
Write short explanatory paragraphs
Avoid burying insights below ads or links
Ensure mobile-friendly layouts

10. Avoid Common LLM Demotion Triggers

Content is less likely to be surfaced if it includes:

Thin or auto-generated filler
Over-promotional language
Excessive affiliate links
Intrusive ads that break structure
Factually incorrect or outdated information

LLMs and AI search systems increasingly filter out content that appears designed only for monetisation.

11. Use llms.txt to Guide AI Systems (Emerging Practice)

An llms.txt file is a plain-text file placed at your domain root that helps AI systems understand:

What your site is about
Which pages are most important
Where high-quality summaries live

Key principles:

Highlight your most valuable content
Use clear headings and Markdown
Include short summaries
Keep it focused and updated

While llms.txt is still an emerging standard, it is widely viewed as future-proofing for AI discovery — similar to early schema adoption. A helpful technical guide can be found here.

Tools to Support LLM Visibility

Structure & Schema

RankMath
Yoast
Schema.org tools

Content Quality & Clarity

SurferSEO
Clearscope
MarketMuse

Entity & Authority

Ahrefs
Moz
Majestic

Freshness & Technical Health

ContentKing
Screaming Frog

AI Support (Editorial Use)

ChatGPT or Claude for outlines, summaries, and QA (with human review)