Generative Engine Optimization Strategies (GEO) for 2025

Generative Engine Optimization (GEO) strategies are the set of practices designed to make content retrievable, re-rankable, and reference-worthy within AI-generated search results, such as Google’s AI Overviews, AI Mode, or ChatGPT. Unlike traditional SEO, which aims to climb SERPs through keywords and technical factors, “GEO SEO” strategies are built around the way large language models (LLMs) fetch, filter, and synthesize information into answers. The goal is not just to appear in search, but to ensure your brand is cited, grounded, and trusted within the AI response itself.

Generative Engine Optimization (GEO) strategies focus on expanding a brand’s semantic footprint, improving fact-density, and enhancing structured data signals so that AI systems recognize and use the content as authoritative input. Ultimately, the aim is to increase AI-driven visibility and business impact, turning brand mentions inside AI conversations into measurable conversions.

Key Takeaways

Generative Engine Optimization Strategies

Unlike traditional SEO, where ranking would hinge on keywords, backlinks, and page authority (these strategies ending their effectiveness in 2019), Genereative Engine Optimization (GEO) is about making your content retrievable, re-rankable, and reference-worthy inside AI-generated answers. This requires aligning your strategy with how LLMs like Google’s AI Overviews, AI Mode, and ChatGPT actually fetch, filter, and ground responses.

1. Expansion of Semantic Footprint Through New Content

AI systems don’t stop at matching keywords—they expand queries into multiple variations through query fan-out, pulling in semantically adjacent concepts (Patent: US11769017B1). To be visible in this process, enterprises must publish content that covers topic clusters, related entities, and adjacent questions rather than just targeting one keyword per page. This builds a broader semantic footprint, ensuring your content is surfaced across more AI-generated query variations.

Determining What New Content Needs to Be Created

Expanding a site’s semantic footprint isn’t about guessing what topics might resonate, it’s about data-driven identification of topical gaps and demand clusters. By combining semantic analysis, log file insights, and competitive benchmarking, enterprises can precisely identify where new content is needed to capture AI-driven visibility.

Step-by-step method:

Don’t have ScreamingFrog? Try Go Fish Digital’s free n-gram analysis Chrome Extension.

Aligning Topics to Pages

The final step is to cross-compare your current sitemap (exporting URLs, Page Titles, and Meta Descriptions into a spreadsheet) against the clusters of user prompts and topical adjacencies. This allows you to:

This alignment process, performed as a topic adjacency mapping exercise, ensures that every high-demand prompt is either assigned to an optimized page or earmarked for new content development.

To see how this is done, scroll to the bottom of this article for a more detailed how-to with accessible tools…

2. Expansion of Semantic Footprint Through Page Optimizations

It’s not enough to create new content, existing pages must also be enriched to maximize passage-level visibility. Recent findings in the industry show rerankers like ret-rr-skysight-v3 (used in ChatGPT) reward comprehensive, high-authority passages over shallow sections. By expanding FAQs, adding contextual internal links, and embedding structured explanations directly into pages, enterprises can grow their semantic density at the page level. This makes it easier for AI models to extract and reuse sections of your content as grounding material.

How-to: Map Topical Adjacencies to Existing Pages and Run a Page-by-Page Gap Analysis

Use the same inputs from your earlier workflow (site + competitor n-grams/bigrams via ScreamingFrog, ChatGPT-User log hits, Ahrefs AIO pages/keywords), then add two focused passes: (A) map adjacencies to your sitemap and (B) do a finer, page-level gap audit.

1) Consolidate inputs into a master sheet

2) Form clusters & adjacencies (query fan-out)

3) Map adjacencies to the sitemap (URL alignment)

4) Select pages for optimization (the “Partial” bucket)

5) Page-by-page “finer-tooth comb” gap analysis

For each Partial Match URL, build a quick coverage matrix:

6) Prioritize edits by “reranker survivability”

Weight recommended edits by: completeness, unique information gain, citation quality, entity coverage, structure (lists/tables/FAQ), freshness, and internal link support—i.e., the signals a reranker will likely reward before synthesis.

7) Implement structured data & feeds

8) Validate & measure

Suggested sheet columns for the mapping pass
Cluster • Representative Prompt • Intent • Entities • Mapped URL • Match Strength (Strong/Partial/None) • Missing Sub-queries • Passage Actions • Schema Needed • Internal Links (To/From) • Last Updated • Evidence (Logs/AIO/Competitor)

This two-stage method turns your sitemap into a semantic optimization blueprint—mapping adjacencies to the right pages first, then closing exact, passage-level gaps that help pages survive retrieval → reranking → grounding and get cited in AI answers.

To see how this is done, scroll to the bottom of this article for a more detailed how-to with accessible tools…

3. Fact-Density Expansion Through Information Gain Analysis

Google’s patents and AI findings consistently highlight the importance of fact-rich, authoritative content (see WO2024064249A1). Models favor content that provides new, verifiable information, which Google has described as information gain. Enterprises should analyze where competitors’ content lacks depth and expand their own pages with statistics, citations, case studies, and unique insights. Increasing fact-density not only improves authority but also raises the likelihood that passages are extracted and cited as “grounding” sources in AI answers.

Performing Information Gain Analysis

One of the most consistent signals across Google’s patents and LLM findings is the value of information gain—providing users with new, verifiable, and fact-dense content rather than repeating what already exists. Patents such as WO2024064249A1 emphasize that content offering unique insights, citations, or supporting data is more likely to be selected as part of a stateful AI response. For enterprises, this means moving beyond surface-level coverage and ensuring every page contains statistical depth, authoritative references, and unique contributions.

How to identify and close information gain gaps:

4. Expansion of Structured Data Through Merchant Feeds or Schema.org Data Sets

LLMs rely on structured data to interpret, extract, and present information reliably. Findings show systems like ChatGPT use vocabulary-aware search to prioritize content that’s clearly tagged and semantically contextualized. By expanding structured signals—through Schema.org markup, product feeds, and entity datasets, brands make it easier for AI models to parse and reuse their information. This aligns with patents around context-aware query classification (US9449105B1) and Google’s MUM initiative, which emphasizes multimodal, cross-domain grounding.

At its core, the strategy here is simple: feed the LLM as much structured data as possible. Imagery, business information, product SKUs, brand relationships, and entity references. The richer your structured footprint, the easier it is for AI systems to surface your content as a trusted reference.

How to expand structured data effectively:

How To Perform Generative Engine Optimization (GEO) Gap Analysis

If you got to this point, you might want a tutorial on how I would do some of the analysis above. Let’s walk through exactly how I did that using some of the tools available on the market as well as an example website, the Go Fish Digital blog.

Disclosure:

Step 1: Start a New ChatGPT Project

Since projects can contain larger data sets for reference (in CSV format). I started a ChatGPT project for this example.

Step 2: Start Exports of N-Gram Analysis

Store crawl as HTML and export 3-gram (4-gram is sometimes even better) analysis into spreadsheets of a given target and competitor gap. Currently using the Go Fish Digital blog to do this:

Bonus: Use the semantic analysis tool inside ScreamingFrog to take this process even further. For the demo, I’m keeping this simple (and can be done using a variety of techniques). To learn how to use the ScreamingFrog semantic analysis, watch this video by Taylor Scher.

Step 3: Upload Multiple CSV Assets Into Your ChatGPT5 Project

Since this is a light analysis for the purposes of the walk through. The more data that’s available the more you can process over the prompts in a type of “bash” way to get to what insights you’re looking for. A few spreadsheets were uploaded:

Step 5: Start to Build Core Topic Coverage

The core topic coverage will highlight some of the major areas missing in terms of semantic footprint before we get into topical adjacencies and the potential coverage of that.

Since the output of the CSV based on n-grams (3-grams) can be a little unclear, the discretionary analysis of this output is key. Taking the existing spreadsheet we can then remove some opportunities from the initial ideas list and create another upload into the project folder with the selected opportunities.

Right away, we can see some pillar topics that we are missing, which could add to our semantic footprint. Since we scoped this only to the blog, it’s not considering service pages that we already have available. So we’re going to refine this list to some target topics that we like. Key point is topics based on our ideal customer persona. That’s really the matrix we’re matching here with the analysis by hand.

Step 6: Start to Build Topic Adjacencies That Will Feed Into Accurate Prompt Examples

For the purposes of this “simpler” demo, I’m going to take the selected opportunities and then start building out topical adjacencies to these subjects, which will help to map out the most potential prompt coverage.

Remember: For larger websites (like in the enterprise-level), this same approach can be taken but needs to be done with larger data sets and should be done using a scripting language vs. ChatGPT. However, just for the illustration purposes of the method, I’m using ChatGPT5 here.

Now that I have my selected core topics, we want to build topical adjacencies for the best prompts that could surface in ChatGPT or AI Mode.

Note: It’s important that we fed in the data assets of the n-grams (and even other data assets) so that the reverse-prompt suggestions that we get will be accurate. Versus simply asking a GPT what questions might come up. It now has a lot more insights (and could have more if we feed it more data) on how to reverse engineer real questions.

Now we can continue to reduce here as we look at this. Since some of these adjacencies make a lot of sense and others don’t:

Step 7: Example Question Prompt Coverage

As you can tell from this whole process, we’re using a data-informed way to determine target demand topics. However, not starting from keyword research. Starting from topic alignment and anticipated prompts (since Users are putting far more information into their “search” using AI tools).

We can ask ChatGPT to now dwindle-down all of the insights it has gathered to give us far more accurate recommendations. In addition, if we wanted to take this further, we could feed in page-level insights from competitors around those adjacencies.

From here we can see a lot of targeted insights that will helpful in determining which new pages or existing pages could be targeted. We could once again, go back and map semantic alignment to existing pages if we had them. But in short, since we are looking at new pages to create, this gives us a much better idea of coverage (semantic footprint) we need to produce in order to increase our citation chances.

Frequently Asked Questions About Generative Engine Optimization (GEO)

Common questions and answers from our experts:

What gaps exist today in Generative Engine Optimization?

The biggest gap is at the prompt level. While traditional SEO gives clear visibility through keyword rankings and SERP impressions, GEO lacks the same transparency—brands don’t always know which prompts their content appears for inside AI systems. Although inputs can be pieced together from log files, semantic analysis, and AIO tracking tools like Ahrefs, this creates a data analysis burden for enterprises. Until purpose-built GEO measurement tools emerge, organizations will need to invest in custom workflows and log-based analysis to close the gap.

Why is content so critical for Generative Engine Optimization?

LLMs are powered by information capture—they can only surface what has been published and structured for them to parse. This makes content the backbone of GEO. To increase visibility, brands must expand their semantic footprint: producing comprehensive, modular, and fact-dense content that answers the full spectrum of user queries. Unlike older SEO strategies where backlinks (these strategies ending in 2018) could compensate for thin content, GEO requires a rich layer of owned media that can serve as authoritative grounding material at every stage of the buyer journey.

Is Generative Engine Optimization going to change?

Yes, GEO will evolve as AI adoption and user search behavior shifts. Already, users are asking longer, deeper, and more task-oriented questions of AI systems. While the methods of retrieval and synthesis may change, the underlying requirement will remain: brands must provide semantically rich, fact-dense, and authoritative content at the domain level to be consistently surfaced. In other words, while the mechanics of GEO may adapt, the foundation of semantic breadth and factual authority will continue to define visibility in AI-driven search.

More on AI search from Go Fish Digital