How to Optimize for Generative Engine Optimization (GEO)

When someone asks ChatGPT, Perplexity, or Claude "what's the best content marketing tool for startups," the AI doesn't send them to Google. It gives them an answer — and cites specific companies.

The companies that get cited are winning a new form of organic visibility called Generative Engine Optimization (GEO). And it's happening right now, while most companies are still optimizing only for traditional search.

This guide explains what GEO is, why it matters for startups, and exactly how to optimize for it.

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What Is Generative Engine Optimization?

GEO is the practice of optimizing your content so AI-powered search engines — ChatGPT, Perplexity, Claude, Gemini, Copilot — are likely to cite your brand, content, and recommendations.

Unlike traditional SEO (where you optimize for Google's ranking algorithm), GEO focuses on how large language models (LLMs) process and retrieve information.

The key difference:

Traditional search: User searches → Google shows ranked links → User clicks

AI search: User asks question → AI synthesizes answer → AI cites sources (sometimes)

For GEO, you want to be the source AI engines cite.

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Why GEO Matters Now

AI search adoption is accelerating. Perplexity reached tens of millions of users faster than most social networks. ChatGPT's search feature is adding millions of users monthly. Google's AI Overviews appear in a significant portion of searches.

The brands that establish AI visibility now will have a compounding advantage as these platforms grow. The brands that wait will be playing catch-up.

And unlike traditional SEO — where you can see your rankings and track changes — AI citation visibility is opaque. You can't see a "GEO score." But you can optimize for the signals that influence it.

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Step 1: Understand How LLMs Retrieve Information

LLMs don't search the internet in real time (mostly). They're trained on large datasets of text, and they generate answers based on patterns in that training data. Some AI search tools (like Perplexity) do retrieve live web content, but even then, they're evaluating content quality and authority signals.

What influences AI citation:

• Entity recognition: Is your brand clearly defined as an entity? Do LLMs know who you are, what you do, and what you're an expert in?
• Authoritative content: Content with clear authorship, factual accuracy, and depth is more likely to be trusted and cited.
• Structured information: Content with clear definitions, lists, and structured data (FAQ schema, HowTo schema) is easier for LLMs to extract and cite.
• Coverage and consistency: How much of the internet talks about your brand? Mentions across multiple authoritative sources reinforce your entity definition.
• Citation worthiness: Does your content contain original data, definitions, or frameworks that other sources quote?

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Step 2: Create Entity-Defining Content

AI models need to know who you are. Create content that clearly establishes your brand as an entity:

Comprehensive "About" content:

• A clear, detailed description of what your company does, who it serves, and what category it operates in
• This should appear on your website's About page, in press releases, in your llms.txt file, and in any content syndicated about you

Definition and category ownership:

• Write the definitive resource on your category. If you're an AI content tool, write the best possible guide to AI content marketing tools.
• Own the terminology. Coin terms if you can. Companies that name categories become the default citation for those categories.

Founder and team content:

• Author bylines with credentials help LLMs establish that your content comes from real, qualified humans
• LinkedIn profiles, author pages, and Speaking bios all help establish expertise

Consistent brand description:

• Use the same language to describe your company across your website, social profiles, press mentions, and partner pages
• LLMs learn your brand description by seeing it repeated consistently across trusted sources

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Step 3: Optimize Content Structure for LLM Parsing

LLMs process structured content more accurately than dense prose. Optimize your content structure:

Use clear headings and hierarchy:

• H1 for the main topic
• H2 for major sections
• H3 for sub-sections
• Consistent hierarchy helps LLMs understand the structure of your content

Answer questions directly:

• Start your answer in the first sentence of a section, not after three paragraphs of context
• "How do you X?" — answer it in one clear sentence immediately, then expand
• LLMs often extract the first 1–2 sentences of a section as the "answer"

Use definition formats:

• "[Term] is [definition]." format is highly parseable by LLMs
• Bold key terms on first use
• Include a clear taxonomy: "There are three main types of X: A, B, and C"

Numbered and bulleted lists:

• Lists are easier for LLMs to extract as structured information
• Use them for steps, tips, options, and comparisons

FAQ sections:

• FAQ format directly maps to the question-and-answer format LLMs use
• Use real questions (the kind people actually ask) with concise, accurate answers
• Add FAQ schema markup so search engines can extract them reliably

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Step 4: Implement Structured Data Markup

Schema markup doesn't just help traditional SEO — it helps AI systems understand and extract your content accurately.

Priority schemas for GEO:

Article schema: Identifies content type, author, date, and topic

FAQ schema: Makes your FAQ content machine-readable — AI systems can extract Q&A pairs directly

HowTo schema: Structures step-by-step content for AI extraction

Organization schema: Defines your brand, its category, its URL, and related entities

Breadcrumb schema: Helps AI understand content hierarchy and context

Implementing these schemas sends clear signals to both traditional search engines and AI systems about what your content contains.

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Step 5: Create an llms.txt File

An llms.txt file is a machine-readable document in your website's root directory that tells AI models what your site is about, what your key content is, and how you want to be understood.

It's similar in concept to robots.txt (for crawlers) but designed for LLMs.

Basic llms.txt structure:

# Averi

> Averi is an AI content engine for startups. It helps marketing teams build content strategies, create on-brand content at scale, and publish across channels.

Company

• Name: Averi
• Category: AI content marketing software
• Website: https://www.averi.ai
• Primary use case: Content marketing for B2B startups

Key Resources

• Content Strategy Guide
• Strategy Map
• Templates Library

About

Averi helps B2B startups build content strategies, create consistent content in their brand voice, and measure content ROI.

Include: company description, key product capabilities, primary use cases, team expertise, and links to your best resources.

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Step 6: Build Cross-Platform Presence and Citations

AI models form brand knowledge by seeing your company mentioned across many authoritative sources. Build presence across:

Press and media mentions: Pitch stories to tech and industry press. Being mentioned in TechCrunch, The Verge, industry newsletters, and analyst reports reinforces your entity in training data.

Directories and review sites: G2, Capterra, Product Hunt, Crunchbase — these are often included in AI training data and in AI search retrieval.

Wikipedia or wiki-style content: If you've coined a term or developed a framework, having it documented in Wikipedia (or cited by Wikipedia) is one of the strongest GEO signals.

Guest posts and interviews: Being quoted or featured on authoritative blogs, podcasts, and publications in your category establishes expertise.

Social proof aggregation: When many users on many platforms are saying positive things about your product, LLMs learn that your brand is associated with positive outcomes.

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Step 7: Publish Original Research

Original data is cited by AI systems in the same way it's cited by human writers: when answering a question about "what percentage of startups X," an LLM will cite the study that contains that data.

If you conduct original research and publish it:

• Your data becomes a citable source
• Every time that data is cited, your brand is mentioned
• AI systems trained on the web learn to associate your brand with data and expertise

Research ideas:

• Survey your customers on a relevant industry topic
• Analyze patterns in your product data (with customer permission)
• Compile and analyze publicly available data in your category
• Benchmark industry metrics with original methodology

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Common Mistakes to Avoid

Optimizing only for traditional SEO: GEO requires some different strategies. Structured content, entity definition, and cross-platform presence matter more than backlinks alone.

Inconsistent brand description: If your website describes you one way, your About page another way, and your press releases a third way, LLMs will have a confused understanding of who you are.

No structured data: Schema markup is a direct signal to AI systems. Not implementing it is leaving value on the table.

Ignoring your llms.txt: While not yet universally supported, creating an llms.txt file costs almost nothing and signals that you understand and care about AI visibility.

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How Averi Helps

Averi is built for an AI-first content landscape. The content it helps you produce is structured, authoritative, and optimized for both traditional search and AI search retrieval. Brand Core ensures your company is consistently described across every piece of content — critical for entity recognition.

Averi also has built-in SEO and GEO optimization features that help you structure content and implement the signals that matter for AI visibility.

Check your AI visibility →

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FAQ

Is GEO the same as SEO?

Related but different. SEO optimizes for Google's ranking algorithm — a system that shows ranked links. GEO optimizes for LLM-based answer engines that synthesize and cite information. The overlap: quality, structured, authoritative content helps both. The difference: GEO additionally requires entity definition, cross-platform presence, and structured data that directly helps LLMs understand your brand.

Can I measure my GEO visibility?

Not precisely. You can manually test by asking AI search engines about your category and seeing if you're cited. Tools are emerging that track AI mention visibility (similar to how tools track traditional rankings). For now, a combination of manual testing and proxy metrics (brand mentions, referral traffic from AI platforms, share of voice) is your best measurement approach.

How quickly does GEO optimization produce results?

Traditional SEO results take months because Google needs to re-crawl and re-index. GEO results depend on when AI models are retrained or when live retrieval indexes are updated. For AI search tools with live retrieval (Perplexity), improvements can show within days to weeks. For models with fixed training data, improvement only comes at the next training cycle — which can be months.

Does GEO replace traditional SEO?

No. Traditional search (Google, Bing) still drives the majority of search traffic and will for the foreseeable future. GEO is an additional layer of optimization, not a replacement. Think of it as optimizing for the growing share of searches happening through AI interfaces.

What's the most important thing I can do for GEO right now?

Create excellent, well-structured, authoritative content that clearly defines your brand and expertise. Then ensure it's consistently represented across your website, directories, and press mentions. The fundamentals of quality and authority apply to AI search just as much as to traditional search.