What is Generative Engine Optimization?

Generative Engine Optimization: More Than Just a Buzzword

Some marketers perceive Generative Engine Optimization (GEO) as merely a fleeting trend. However, this assumption is flawed because search powered by Large Language Models (LLMs) is rapidly gaining traction.

A growing number of websites are witnessing a decline in clicks due to Google’s AI Overviews, while tools like ChatGPT, Gemini, and Perplexity are reshaping how users seek information.

If your strategy doesn’t include optimizing for AI search, you’re likely missing out on valuable traffic that could translate into conversions.

At Exposure Ninja, we’ve been actively experimenting with GEO across various industries, testing methods that effectively enhance visibility on these emerging platforms.

In this guide, we’ll outline the practical strategies we employ to optimize for generative search and explain how you can adopt our process to boost your brand’s presence in AI search features.

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization is a strategy aimed at tailoring your content for LLM tools such as Google’s AI Overviews, ChatGPT, Gemini, Perplexity, and other generative models that provide answers instead of links.

Differences Between GEO and Traditional SEO

While GEO builds upon your existing SEO strategy, the signals you optimize for and how success is measured differ significantly.

How Do Generative Engines Work?

To get your content featured in generative search, it’s essential to understand how these engines generate responses. Unlike traditional search, which scans for keywords, generative engines analyze queries to identify the best sources and produce unique information each time.

Generative Engines Rewrite Queries

When users input lengthy or complex questions into an AI tool like ChatGPT or Perplexity, the system breaks the query into smaller, search-friendly components. These components are then processed using internal training data and, depending on the platform, external web sources.

The model summarizes the results and reassembles them into a cohesive response. This allows users to search using longer sentences, multiple thoughts, or even entire paragraphs, as the engine deconstructs the input into manageable chunks.

Perplexity, for example, provides transparency by showing its processes. It conducts multiple web searches behind the scenes and cites sources directly in the output, making it easier to comprehend how information is gathered and synthesized into a final answer.

Variations in Outputs Based on Training and Access

Not all generative engines operate identically. Some rely more on web results, while others depend on pre-trained datasets. Additionally, they utilize different search engines or crawling methods internally.

Since these platforms use distinct data and methodologies to retrieve and summarize content, the same question may yield varying answers depending on where it’s asked.

Even when sources are cited, those citations can differ, underscoring the need for AI optimization to be broad enough to span multiple platforms.

Generated Outputs, Not Copied Responses

Generative engines don’t copy and paste from databases. Instead, they create new responses dynamically based on patterns in the data. While the same page might be referenced across multiple searches, the language generated is unique each time.

When personalization is enabled, LLM engines like ChatGPT can tailor responses to align with a user’s search history or preferences, adding further variety to the results.

Identifying Opportunities to Optimize for AI Overviews

If you’re already tracking organic rankings using STAT, uncovering AI Overview (AIO) opportunities is just a few steps away. Here’s the workflow:

Step 1: Focus on Keywords Already Ranking in the Top 10

Begin by narrowing your dataset to keywords where your site ranks in positions 1–10. This establishes a foundation of content that’s close to success, even if it hasn’t yet appeared in an AI Overview.

Step 2: Apply the AI Overview SERP Feature Filter

Next, isolate the top 10 keywords that trigger an AI Overview by applying a SERP feature filter for “AI Overview.”

Step 3: Categorize Owned vs. Unowned AIOs

Divide your list into two categories:

• Owned AIOs: Your content is cited within the AI Overview.
• Unowned AIOs: An AI Overview exists, but your content isn’t included.

Step 4: Prioritize High-Value, Commercial-Intent Queries

Create a list of target keywords to optimize for AI Overviews, focusing on those tied to revenue:

• Keywords with buying or research intent.
• Branded product or service queries.
• Pages with high conversion potential.

Strategies to Appear in Generative AI Searches

Here’s the checklist we use at Exposure Ninja to help clients earn citations in AI results:

Precise Content Matching

We ensure the content we publish closely matches the information we aim to rank for. Answer questions directly and use bullet points to highlight key details.

Simplify your content for readability. Just as people struggle with overly complex information, AI finds it harder to interpret unstructured content. It relies on recognizable patterns and clear language to accurately process your message.

One significant change we implemented was rewriting introductory paragraphs to be straightforward and easy for AI to interpret. We also structured key sections using bullet points to emphasize specific takeaways aligned with common AI Overview questions. This helped us anticipate follow-up queries users might ask.

To enhance credibility, we personalized the content by featuring a mortgage professional as the author, who wrote from a first-person perspective. The author’s expertise improved the E-E-A-T (Experience, Expertise, Authority, Trust) of the content, a crucial signal of quality.

For more insights, check out this excellent explanation from Moz on optimizing content for E-E-A-T.

Rank Well in Organic and Local Search

Visibility in traditional search is essential to earning citations in generative search. Without a strong baseline, it’s unlikely your content will appear in AI Overviews or be referenced by generative engines.

Local SEO also plays a critical role. Google’s map packs, business citations, and optimized service pages significantly impact inclusion, especially on platforms like Gemini.

Monitor Referral Traffic from AI Platforms

Use GA4 to filter and group traffic originating from ChatGPT, Bing, Gemini, and other AI engines.

Measure Sessions and Conversions from AI Referrals

While specific keywords in generative search can’t be tracked, outcomes can. In Looker Studio, we surface GEO landing pages and identify which AI platforms drive the most conversions.

Analyze Brand Sentiment and Accuracy in AI Platforms

Beyond traffic, it’s vital to assess how generative search platforms portray your brand and whether the information is accurate.

We conduct manual brand audits within ChatGPT, Gemini, and Perplexity to check for:

• Correct or outdated descriptions.
• Missing key product information.
• Conflicting messages across platforms.

This helps us identify areas to refine messaging, address content gaps, or correct misinformation before it spreads. For clarity, we summarize findings in client-facing reports with example queries and screenshots.

Mistakes to Avoid When Optimizing for Generative Search

Getting cited in generative engines doesn’t follow the same playbook as SEO. In fact, some tactics effective in traditional search won’t enhance visibility in ChatGPT, Gemini, or Perplexity.

Here’s what to avoid:

• Assuming SEO Rankings Equal AI Visibility: Ranking well in Google doesn’t guarantee inclusion in generative answers. AI engines use different inputs and prioritize patterns over positions.
• Using Visual Elements as Key Answers: Screenshots, diagrams, or product images rarely appear in AI chat results. If the value isn’t in the text, it likely won’t be seen.
• Duplicating Answers Across Pages: Repeating the same answer on a page or across multiple pages can confuse large language models, making it harder to detect relevance and intent.
• Publishing High Volumes of Unedited AI Content: Quantity doesn’t impress AI engines. Unedited or templated content is more likely to be ignored or flagged as low value.
• Spamming LLMs with Repeated Brand Mentions: Stuffing brand phrases excessively won’t improve visibility. Generative engines look for natural signals across credible sources.
• Relying on Keywords Instead of Authority: Focusing solely on keywords without building brand credibility won’t yield much value from generative platforms.

Concluding Thoughts: Start Optimizing for Generative Engines Today

You don’t have to choose between GEO or SEO; optimize for both. Use STAT to uncover AI Overview opportunities, invest in digital PR to increase brand mentions, and track the right performance metrics.