Generative Engine Optimization (GEO) Guide

The new search landscape: AI is changing how people find information

For 25 years, "getting found online" meant ranking on Google. You optimized for keywords, built backlinks, and competed for those precious first-page results.

That's changing fast.

Today, millions of people ask ChatGPT, Claude, Perplexity, Google's AI Overviews, and Bing Copilot for recommendations, explanations, and answers. Instead of clicking through ten blue links, they get a single, synthesized answer—often mentioning specific brands, products, or websites.

The question is: Is your brand in those answers?

This guide explains the new world of AI-powered search in plain English. You'll learn how AI systems find information, why some brands appear in AI answers while others don't, and practical strategies to increase your visibility—while protecting your reputation.

What is GEO, AEO, and AI SEO? (The jargon, translated)

The marketing industry loves creating new acronyms. Here's what they actually mean:

Generative Engine Optimization (GEO)

Plain English: Making your brand more likely to appear when people ask AI chatbots questions.

"Generative engines" are AI systems that generate text answers instead of showing lists of links. ChatGPT, Claude, Perplexity, and Google's AI Overviews are all generative engines.

GEO is about optimizing your online presence so these AI systems mention you in their answers.

Answer Engine Optimization (AEO)

Plain English: Structuring your content so AI systems can easily extract and use it to answer questions.

This is similar to GEO but focuses specifically on getting your content used as the source of answers. Think of it as "being quotable by AI."

AI SEO / AI Search Optimization

Plain English: The broader practice of optimizing for any AI-powered search experience.

This includes both traditional search engines with AI features (Google AI Overviews, Bing Copilot) and pure AI chatbots (ChatGPT, Claude).

What's the difference?

Honestly? These terms overlap significantly. Marketers use them somewhat interchangeably. The core idea is the same: make your brand visible in AI-generated answers, not just traditional search results.

For simplicity, this guide uses "GEO" to cover all of these concepts.

How AI systems find information: Three different approaches

This is critical to understand. Not all AI systems work the same way, and the approach they use dramatically affects how (or whether) they mention your brand.

Approach 1: Trained knowledge only (no search)

Examples: Base ChatGPT (without browsing), Claude (without web search), most local AI models

How it works:

The AI was trained on a massive dataset of text from the internet, books, and other sources—but that training has a cutoff date. When you ask a question, the AI generates an answer based purely on what it "learned" during training.

What this means for your brand:

• The AI only knows about you if you were prominent in its training data
• Training data often comes from Wikipedia, news sites, Reddit, forums, academic papers, and popular websites
• Recent information (after the training cutoff) doesn't exist to the AI
• If your brand wasn't well-represented in the training data, the AI may not know you exist
• The AI might have outdated or incorrect information about you

Example:

User: "What's the best project management tool for small teams?"

AI (trained knowledge only): "Popular options include Asana, Trello, Monday.com, and Basecamp. Each has different strengths..."

The AI recommends brands it learned about during training. Newer tools or smaller brands may not appear.

Approach 2: Search-first (always searches before answering)

Examples: Perplexity, Google AI Overviews, Bing Copilot

How it works:

Before generating an answer, the AI automatically searches the web, retrieves relevant pages, and then synthesizes an answer based on those search results. The AI cites its sources and bases answers on current information.

What this means for your brand:

• Your current web presence directly affects whether you appear in answers
• Traditional SEO still matters—if you rank well in search, you're more likely to be included
• The AI pulls from what it finds right now, not what it was trained on
• You can influence results by publishing helpful, well-structured content
• Negative content (bad reviews, complaints) may also be found and mentioned

Example:

User: "What's the best project management tool for small teams in 2025?"

AI (search-first): "Based on recent reviews and comparisons, top options include [cites sources]: ClickUp for feature depth, Notion for flexibility, Linear for development teams..."

The answer reflects current search results, including newer tools and recent reviews.

Approach 3: Search-enabled (can search when needed)

Examples: ChatGPT with browsing enabled, Claude with web search, Gemini

How it works:

The AI has both trained knowledge and the ability to search the web. It decides whether to search based on the question—sometimes using just its training, sometimes searching, sometimes combining both.

What this means for your brand:

• Unpredictable whether the AI will use trained knowledge or search
• For current topics (news, recent events), it's more likely to search
• For established topics, it may rely on trained knowledge
• Your brand needs visibility in both: training data sources AND current search results

Example:

User: "What's the best project management tool for small teams?"

AI (search-enabled): May use trained knowledge for general recommendations, but might search if the user asks about recent features, pricing, or comparisons.

Where does AI training data come from?

Understanding the sources helps you understand where to build presence:

Common training data sources

The Reddit factor

Reddit deserves special attention. AI training datasets heavily feature Reddit discussions because:

• Real people sharing genuine experiences
• Question-and-answer format is easy for AI to learn from
• Covers almost every topic imaginable
• Subreddits provide topical organization

What this means: If people on Reddit regularly recommend your product (or complain about it), that sentiment likely influenced AI training. AI systems often reflect the "wisdom of Reddit" in their recommendations.

How to get your brand mentioned in AI answers

Now the practical part. Here are strategies organized by effort and impact:

Foundation: Be worth mentioning

Before any optimization, ask honestly: Would a knowledgeable human expert recommend your product?

AI systems are designed to be helpful. They tend to recommend products and brands that are:

• Well-reviewed by real users
• Genuinely useful for the stated purpose
• Not associated with scams, controversies, or poor quality

If your product isn't good enough to be recommended by humans, optimizing for AI won't help. Fix the product first.

Strategy 1: Strengthen your Wikipedia presence

Why it matters: Wikipedia is a primary source for AI training data. A well-written Wikipedia entry significantly increases AI awareness of your brand.

What to do:

• If notable enough, ensure your company/product has a Wikipedia page
• Follow Wikipedia's notability guidelines strictly—don't try to game the system
• Ensure the page is accurate, up-to-date, and well-sourced
• Don't edit your own Wikipedia page (it's against Wikipedia policy)
• Provide journalists and publications with accurate information they can cite

Reality check: Wikipedia has strict notability requirements. Most small businesses won't qualify. Focus on other strategies if this doesn't apply.

Strategy 2: Create genuinely helpful content

Why it matters: AI systems learn to associate brands with topics based on helpful content. If you consistently publish useful information about a topic, AI is more likely to mention you when discussing that topic.

What to do:

• Write comprehensive guides, tutorials, and explanations in your area of expertise
• Answer common questions thoroughly (not superficially for SEO)
• Include specific examples, data, and practical advice
• Structure content clearly with headings, lists, and tables (AI can parse these easily)
• Publish on your own site and contribute to authoritative industry publications

Example: A cybersecurity company that publishes excellent guides on password security, phishing prevention, and data protection is more likely to be mentioned when AI discusses these topics.

Strategy 3: Earn genuine coverage and mentions

Why it matters: AI training includes news articles, industry publications, and expert recommendations. Genuine coverage from trusted sources reinforces your authority.

What to do:

• Focus on earning press coverage through newsworthy announcements and genuine innovation
• Contribute expert commentary to industry publications
• Participate in industry research and reports
• Speak at conferences and events that get coverage
• Build relationships with journalists and analysts who cover your space

Avoid: Paying for fake reviews, astroturfing, or manufactured endorsements. AI systems are increasingly trained to recognize (and distrust) inauthentic content.

Strategy 4: Build positive presence on Reddit and forums

Why it matters: User discussions heavily influence AI training. Genuine recommendations from real users carry significant weight.

What to do:

• Encourage satisfied customers to share their experiences (without incentivizing fake reviews)
• Have team members participate authentically in relevant communities (with disclosure)
• Respond helpfully to questions about your product category
• Address complaints and negative feedback constructively
• Monitor discussions to understand how people perceive your brand

Critical rule: Never fake user reviews or astroturf. This is unethical, often illegal, and increasingly detectable. It will damage your reputation more than help it.

Strategy 5: Optimize for search-first AI systems

Why it matters: Systems like Perplexity and Google AI Overviews search the web before answering. Traditional SEO directly affects whether you appear.

What to do:

• Maintain strong traditional SEO (rankings, backlinks, site health)
• Structure content with clear headings and direct answers to questions
• Use schema markup (FAQ, HowTo, Product) to help AI understand your content
• Ensure your site is fast, mobile-friendly, and accessible
• Keep information current and accurate

Strategy 6: Monitor and respond to AI mentions

Why it matters: You can't improve what you don't measure.

What to do:

• Regularly test how AI systems respond to queries about your brand and category
• Document what AI says about you and competitors
• Track changes over time
• Use insights to guide content and PR strategy

How to test:

Ask various AI systems questions like:

• "What is [your brand]?"
• "What's the best [product category]?"
• "[Your brand] vs [competitor]?"
• "Should I use [your brand]?"
• "Problems with [your brand]?"

Managing brand reputation in AI answers

This is crucial: AI systems can and do reflect negative information about brands. If there's significant negative sentiment online, AI may warn users or exclude your brand from recommendations.

How AI systems handle brand reputation

AI systems are trained to be helpful and honest. This means:

• They reflect reality: If your product has serious problems that are widely discussed, AI will likely mention them
• They synthesize sentiment: AI weighs multiple sources. Overwhelmingly positive sentiment leads to positive mentions; mixed or negative sentiment leads to warnings or exclusions
• They prioritize user safety: AI systems are cautious about recommending products with safety concerns, ethical issues, or patterns of customer harm
• They may exclude problematic brands: Some AI systems (like Claude) actively avoid recommending brands associated with harm, deception, or serious ethical violations

Real example: How reputation affects AI recommendations

Scenario: A user asks "What's the best [product category]?"

Brand with good reputation: AI mentions the brand positively, highlights strengths, may recommend it.

Brand with mixed reputation: AI might mention the brand with caveats ("Some users report issues with customer service...") or list it among options without strong endorsement.

Brand with serious reputation problems: AI may exclude the brand entirely, mention it only to warn about problems, or explicitly recommend alternatives.

Proactive reputation management for AI

Principle: The best defense is a good offense. Building genuine positive reputation is far more effective than trying to suppress negative information.

What to do:

1. Deliver excellent products and service. This is the foundation. No amount of marketing fixes a bad product.

2. Respond constructively to criticism. How you handle complaints matters. Defensive or dismissive responses create more negative content; helpful responses can turn critics into advocates.

3. Fix problems publicly. When you make mistakes, acknowledge them, explain what you're doing to fix them, and follow through. This creates positive content that balances negative.

4. Build a body of positive evidence. Genuine case studies, customer stories, industry awards, and expert endorsements all contribute to positive sentiment.

5. Address inaccuracies directly. If AI systems are stating factually incorrect information about your brand, you may be able to address the underlying sources (outdated articles, incorrect Wikipedia entries, etc.).

When AI gets it wrong

Sometimes AI states incorrect information about your brand. Options include:

• Source correction: Find where the incorrect information originates (often Wikipedia, outdated articles, or misunderstood content) and work to correct it at the source
• Counter-content: Create clear, authoritative content with correct information that may be picked up in future training or search
• Direct feedback: Some AI providers have feedback mechanisms. Use them to report factual errors (this is different from trying to suppress legitimate criticism)
• Legal options: In cases of clear defamation or factual falsehood, legal remedies may apply—but this is a last resort and rarely appropriate

Important: Trying to suppress legitimate criticism or negative reviews typically backfires. AI systems are designed to find and synthesize multiple sources. You can't game the system by hiding negative content.

What doesn't work (and may hurt you)

Tactics to avoid

The authenticity principle

AI systems are specifically designed to surface helpful, accurate information and filter out manipulation. The more sophisticated AI becomes, the harder it is to game.

Long-term winning strategy: Be genuinely good, create genuinely helpful content, earn genuine recommendations. This is harder than tricks, but it's the only approach that works sustainably.

Quick-start checklist

Use this to assess and improve your GEO position:

Audit your current AI visibility

• Test how major AI systems (ChatGPT, Claude, Perplexity, Google AI) describe your brand
• Test how they answer "best [your category]" questions—are you mentioned?
• Search for and read what Reddit/forums say about your brand
• Review your Wikipedia presence (if applicable)
• Check for outdated or incorrect information about your brand online

Build your foundation

• Ensure your product genuinely deserves recommendation
• Create or update your Wikipedia page (if notable enough)
• Publish 3-5 comprehensive, genuinely helpful guides in your expertise area
• Develop a plan to earn press coverage and industry recognition
• Establish authentic presence in relevant online communities

Optimize for findability

• Structure content with clear headings and direct answers
• Implement relevant schema markup (FAQ, HowTo, Product, Organization)
• Ensure strong traditional SEO fundamentals
• Keep all online information accurate and current
• Make your unique value proposition crystal clear

Protect your reputation

• Set up monitoring for brand mentions across platforms
• Create a process for responding to criticism constructively
• Build a library of positive case studies and testimonials
• Document and address any factual inaccuracies you find
• Develop crisis communication plans for potential reputation issues

The bottom line

GEO isn't about tricking AI systems. It's about being genuinely good—and making sure AI can see that.

The brands that succeed in AI-powered search are the ones that:

• Deliver excellent products and experiences
• Create genuinely helpful content
• Earn authentic recommendations
• Maintain transparent, honest communication
• Address problems openly and constructively

This isn't new advice. It's the same approach that builds lasting businesses. AI just makes it more important—because AI is very good at synthesizing the truth about your brand from thousands of sources.

If that truth is positive, AI will help you. If it's negative, AI will reflect that too.

Focus on being worth recommending. The AI mentions will follow.

Want to go deeper?

This guide covers the fundamentals of AI search visibility. For related topics:

• Guide: AI for Marketing — Using AI in your marketing strategy
• Guide: Evaluating AI Answers — Understanding when to trust AI responses
• Glossary: RAG (Retrieval-Augmented Generation) — How AI systems search and retrieve information
• Glossary: Grounding — How AI connects answers to real sources
• Glossary: Hallucination — When AI makes things up

License & Attribution

This resource is licensed under Creative Commons Attribution 4.0 (CC-BY). You're free to:

• Share with your marketing team
• Use in client presentations
• Adapt for your industry
• Build upon for your own guides

Just include this attribution:

"Generative Engine Optimization (GEO) Guide" by Field Guide to AI (fieldguidetoai.com) is licensed under CC BY 4.0